2023
|
Plesh, Richard; Peer, Peter; Štruc, Vitomir GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling Inproceedings In: Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) , 2023. @inproceedings{PleshCVPR2023,
title = {GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling},
author = {Richard Plesh and Peter Peer and Vitomir Štruc},
year = {2023},
date = {2023-06-18},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) },
abstract = {We present GlassesGAN, a novel image editing framework for custom design of glasses, that sets a new standard in terms of image quality, edit realism, and continuous multi-style edit capability. To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearance discovery in the latent space of a pre-trained GAN generator, constructs an eyeglasses-specific (latent) subspace that the editing framework can utilize. Additionally, we also introduce an appearance-constrained subspace initialization (SI) technique that centers the latent representation of the given input image in the well-defined part of the constructed subspace to improve the reliability of the learned edits. We test GlassesGAN on two (diverse) high-resolution datasets (CelebA-HQ and SiblingsDB-HQf) and compare it to three state-of-the-art competitors, i.e., InterfaceGAN, GANSpace, and MaskGAN. The reported results show that GlassesGAN convincingly outperforms all competing techniques, while offering additional functionality (e.g., fine-grained multi-style editing) not available with any of the competitors. The source code will be made freely available.},
keywords = {eyewear, eyewear personalization, face editing, GAN inversion, latent space editing, StyleGAN2, synthetic appearance discovery, targeted subspace modeling, virtual try-on},
pubstate = {published},
tppubtype = {inproceedings}
}
We present GlassesGAN, a novel image editing framework for custom design of glasses, that sets a new standard in terms of image quality, edit realism, and continuous multi-style edit capability. To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearance discovery in the latent space of a pre-trained GAN generator, constructs an eyeglasses-specific (latent) subspace that the editing framework can utilize. Additionally, we also introduce an appearance-constrained subspace initialization (SI) technique that centers the latent representation of the given input image in the well-defined part of the constructed subspace to improve the reliability of the learned edits. We test GlassesGAN on two (diverse) high-resolution datasets (CelebA-HQ and SiblingsDB-HQf) and compare it to three state-of-the-art competitors, i.e., InterfaceGAN, GANSpace, and MaskGAN. The reported results show that GlassesGAN convincingly outperforms all competing techniques, while offering additional functionality (e.g., fine-grained multi-style editing) not available with any of the competitors. The source code will be made freely available. |
Ivanovska, Marija; Štruc, Vitomir Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models Inproceedings In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), pp. 1-6, 2023. @inproceedings{IWBF2023_Marija,
title = {Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models},
author = {Marija Ivanovska and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/03/IWBF2023_Morphing.pdf},
year = {2023},
date = {2023-02-28},
booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},
pages = {1-6},
abstract = {Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion--based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets.},
keywords = {biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face morphing attack, morphing attack, morphing attack detection},
pubstate = {published},
tppubtype = {inproceedings}
}
Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion--based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets. |
Babnik, Žiga; Damer, Naser; Štruc, Vitomir Optimization-Based Improvement of Face Image Quality Assessment Techniques Inproceedings In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), 2023. @inproceedings{iwbf2023babnik,
title = {Optimization-Based Improvement of Face Image Quality Assessment Techniques},
author = {Žiga Babnik and Naser Damer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/03/IWBF_23___paper-1.pdf},
year = {2023},
date = {2023-02-28},
booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},
abstract = {Contemporary face recognition~(FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (real-world) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process. While existing FIQA techniques are able to efficiently capture the differences between high and low quality images, they typically cannot fully distinguish between images of similar quality, leading to lower performance in many scenarios. To address this issue, we present in this paper a supervised quality-label optimization approach, aimed at improving the performance of existing FIQA techniques. The developed optimization procedure infuses additional information (computed with a selected FR model) into the initial quality scores generated with a given FIQA technique to produce better estimates of the ``actual'' image quality. We evaluate the proposed approach in comprehensive experiments with six state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace, SER-FIQ) on five commonly used benchmarks (LFW, CFP-FP, CPLFW, CALFW, XQLFW) using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with highly encouraging results. },
keywords = {distillation, face, face image quality assessment, face image quality estimation, face images, optimization, quality, transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Contemporary face recognition~(FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (real-world) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process. While existing FIQA techniques are able to efficiently capture the differences between high and low quality images, they typically cannot fully distinguish between images of similar quality, leading to lower performance in many scenarios. To address this issue, we present in this paper a supervised quality-label optimization approach, aimed at improving the performance of existing FIQA techniques. The developed optimization procedure infuses additional information (computed with a selected FR model) into the initial quality scores generated with a given FIQA technique to produce better estimates of the ``actual'' image quality. We evaluate the proposed approach in comprehensive experiments with six state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace, SER-FIQ) on five commonly used benchmarks (LFW, CFP-FP, CPLFW, CALFW, XQLFW) using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with highly encouraging results. |
Grm, Klemen; Ozata, Berk; Struc, Vitomir; Ekenel, Hazim K. Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition Inproceedings In: WACV workshops, pp. 120-129, 2023. @inproceedings{WACVW2023,
title = {Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition},
author = {Klemen Grm and Berk Ozata and Vitomir Struc and Hazim K. Ekenel},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/01/Meet_in_the_middle.pdf
https://arxiv.org/abs/2211.15225
https://openaccess.thecvf.com/content/WACV2023W/RWS/papers/Grm_Meet-in-the-Middle_Multi-Scale_Upsampling_and_Matching_for_Cross-Resolution_Face_Recognition_WACVW_2023_paper.pdf
},
year = {2023},
date = {2023-01-06},
booktitle = {WACV workshops},
pages = {120-129},
abstract = {In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset.},
keywords = {deep learning, face, face recognition, multi-scale matching, smart surveillance, surveillance, surveillance technology},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset. |
Pernuš, Martin; Štruc, Vitomir; Dobrišek, Simon MaskFaceGAN: High Resolution Face Editing With Masked GAN Latent Code Optimization Journal Article In: IEEE Transactions on Image Processing, 2023. @article{MaskFaceGAN,
title = {MaskFaceGAN: High Resolution Face Editing With Masked GAN Latent Code Optimization},
author = {Martin Pernuš and Vitomir Štruc and Simon Dobrišek},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/02/MaskFaceGAN_compressed.pdf
https://arxiv.org/pdf/2103.11135.pdf},
year = {2023},
date = {2023-01-02},
journal = {IEEE Transactions on Image Processing},
abstract = {Face editing represents a popular research topic within the computer vision and image processing communities. While significant progress has been made recently in this area, existing solutions: (i) are still largely focused on low-resolution images, (ii) often generate editing results with visual artefacts, or (iii) lack fine-grained control over the editing procedure and alter multiple (entangled) attributes simultaneously, when trying to generate the desired facial semantics. In this paper, we aim to address these issues through a novel editing approach, called MaskFaceGAN that focuses on local attribute editing. The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i.e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially-selective treatment of local image regions. Th constraints are enforced with the help of an (differentiable) attribute classifier and face parser that provide the necessary reference information for the optimization procedure.
MaskFaceGAN is evaluated in extensive experiments on the CelebA-HQ, Helen and SiblingsDB-HQf datasets and in comparison with several state-of-the-art techniques from the literature. Our experimental results show that the proposed approach is able to edit face images with respect to several local facial attributes with unprecedented image quality and at high-resolutions (1024x1024), while exhibiting considerably less problems with attribute entanglement than competing solutions. The source code is publicly available from: https://github.com/MartinPernus/MaskFaceGAN.},
keywords = {CNN, computer vision, deep learning, face editing, face image processing, GAN, generative models, StyleGAN},
pubstate = {published},
tppubtype = {article}
}
Face editing represents a popular research topic within the computer vision and image processing communities. While significant progress has been made recently in this area, existing solutions: (i) are still largely focused on low-resolution images, (ii) often generate editing results with visual artefacts, or (iii) lack fine-grained control over the editing procedure and alter multiple (entangled) attributes simultaneously, when trying to generate the desired facial semantics. In this paper, we aim to address these issues through a novel editing approach, called MaskFaceGAN that focuses on local attribute editing. The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i.e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially-selective treatment of local image regions. Th constraints are enforced with the help of an (differentiable) attribute classifier and face parser that provide the necessary reference information for the optimization procedure.
MaskFaceGAN is evaluated in extensive experiments on the CelebA-HQ, Helen and SiblingsDB-HQf datasets and in comparison with several state-of-the-art techniques from the literature. Our experimental results show that the proposed approach is able to edit face images with respect to several local facial attributes with unprecedented image quality and at high-resolutions (1024x1024), while exhibiting considerably less problems with attribute entanglement than competing solutions. The source code is publicly available from: https://github.com/MartinPernus/MaskFaceGAN. |
Eyiokur, Fevziye Irem; Kantarci, Alperen; Erakin, Mustafa Ekrem; Damer, Naser; Ofli, Ferda; Imran, Muhammad; Križaj, Janez; Salah, Albert Ali; Waibel, Alexander; Štruc, Vitomir; Ekenel, Hazim K. A Survey on Computer Vision based Human Analysis in the COVID-19 Era Journal Article In: Image and Vision Computing, vol. 130, no. 104610, pp. 1-19, 2023. @article{IVC2023,
title = {A Survey on Computer Vision based Human Analysis in the COVID-19 Era},
author = {Fevziye Irem Eyiokur and Alperen Kantarci and Mustafa Ekrem Erakin and Naser Damer and Ferda Ofli and Muhammad Imran and Janez Križaj and Albert Ali Salah and Alexander Waibel and Vitomir Štruc and Hazim K. Ekenel },
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/01/FG4COVID19_PAPER_compressed.pdf
https://authors.elsevier.com/a/1gKOyxnVK7RBS},
doi = {https://doi.org/10.1016/j.imavis.2022.104610},
year = {2023},
date = {2023-01-01},
journal = {Image and Vision Computing},
volume = {130},
number = {104610},
pages = {1-19},
abstract = {The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including
face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks.
Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.},
keywords = {COVID-19, face, face alignment, face analysis, face image processing, face image quality assessment, face landmarking, face recognition, face verification, human analysis, masked face analysis},
pubstate = {published},
tppubtype = {article}
}
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including
face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks.
Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public. |
Hrovatič, Anja; Peer, Peter; Štruc, Vitomir; Emeršič, Žiga Efficient ear alignment using a two-stack hourglass network Journal Article In: IET Biometrics , pp. 1-14, 2023, ISSN: 2047-4938. @article{UhljiIETZiga,
title = {Efficient ear alignment using a two-stack hourglass network},
author = {Anja Hrovatič and Peter Peer and Vitomir Štruc and Žiga Emeršič},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12109},
doi = {10.1049/bme2.12109},
issn = {2047-4938},
year = {2023},
date = {2023-01-01},
journal = {IET Biometrics },
pages = {1-14},
abstract = {Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem of ear alignment is still under-explored in the open literature. Nonetheless, accurate alignment of ear images, especially in unconstrained acquisition scenarios, where the ear appearance is expected to vary widely due to pose and view point variations, is critical for the performance of all downstream tasks, including ear recognition. Here, the authors address this problem and present a framework for ear alignment that relies on a two-step procedure: (i) automatic landmark detection and (ii) fiducial point alignment. For the first (landmark detection) step, the authors implement and train a Two-Stack Hourglass model (2-SHGNet) capable of accurately predicting 55 landmarks on diverse ear images captured in uncontrolled conditions. For the second (alignment) step, the authors use the Random Sample Consensus (RANSAC) algorithm to align the estimated landmark/fiducial points with a pre-defined ear shape (i.e. a collection of average ear landmark positions). The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2-SHGNet model leads to more accurate landmark predictions than competing state-of-the-art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery.},
keywords = {biometrics, CNN, deep learning, ear, ear alignment, ear recognition},
pubstate = {published},
tppubtype = {article}
}
Ear images have been shown to be a reliable modality for biometric recognition with desirable characteristics, such as high universality, distinctiveness, measurability and permanence. While a considerable amount of research has been directed towards ear recognition techniques, the problem of ear alignment is still under-explored in the open literature. Nonetheless, accurate alignment of ear images, especially in unconstrained acquisition scenarios, where the ear appearance is expected to vary widely due to pose and view point variations, is critical for the performance of all downstream tasks, including ear recognition. Here, the authors address this problem and present a framework for ear alignment that relies on a two-step procedure: (i) automatic landmark detection and (ii) fiducial point alignment. For the first (landmark detection) step, the authors implement and train a Two-Stack Hourglass model (2-SHGNet) capable of accurately predicting 55 landmarks on diverse ear images captured in uncontrolled conditions. For the second (alignment) step, the authors use the Random Sample Consensus (RANSAC) algorithm to align the estimated landmark/fiducial points with a pre-defined ear shape (i.e. a collection of average ear landmark positions). The authors evaluate the proposed framework in comprehensive experiments on the AWEx and ITWE datasets and show that the 2-SHGNet model leads to more accurate landmark predictions than competing state-of-the-art models from the literature. Furthermore, the authors also demonstrate that the alignment step significantly improves recognition accuracy with ear images from unconstrained environments compared to unaligned imagery. |
2022
|
Gan, Chenquan; Yang, Yucheng; Zhub, Qingyi; Jain, Deepak Kumar; Struc, Vitomir DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition Journal Article In: Expert Systems with Applications, vol. 210, 2022. @article{TextEmotionESWA,
title = {DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition},
author = {Chenquan Gan and Yucheng Yang and Qingyi Zhub and Deepak Kumar Jain and Vitomir Struc},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422016025?via%3Dihub},
doi = {https://doi.org/10.1016/j.eswa.2022.118525},
year = {2022},
date = {2022-12-30},
urldate = {2022-08-01},
journal = {Expert Systems with Applications},
volume = {210},
abstract = {To balance the trade-off between contextual information and fine-grained information in identifying specific emotions during a dialogue and combine the interaction of hierarchical feature related information, this paper proposes a hierarchical feature interactive fusion network (named DHF-Net), which not only can retain the integrity of the context sequence information but also can extract more fine-grained information. To obtain a deep semantic information, DHF-Net processes the task of recognizing dialogue emotion and dialogue act/intent separately, and then learns the cross-impact of two tasks through collaborative attention. Also, a bidirectional gate recurrent unit (Bi-GRU) connected hybrid convolutional neural network (CNN) group method is designed, by which the sequence information is smoothly sent to the multi-level local information layers for feature exaction. Experimental results show that, on two open session datasets, the performance of DHF-Net is improved by 1.8% and 1.2%, respectively.},
keywords = {attention, CNN, deep learning, dialogue, emotion recognition, fusion, fusion network, nlp, semantics, text, text processing},
pubstate = {published},
tppubtype = {article}
}
To balance the trade-off between contextual information and fine-grained information in identifying specific emotions during a dialogue and combine the interaction of hierarchical feature related information, this paper proposes a hierarchical feature interactive fusion network (named DHF-Net), which not only can retain the integrity of the context sequence information but also can extract more fine-grained information. To obtain a deep semantic information, DHF-Net processes the task of recognizing dialogue emotion and dialogue act/intent separately, and then learns the cross-impact of two tasks through collaborative attention. Also, a bidirectional gate recurrent unit (Bi-GRU) connected hybrid convolutional neural network (CNN) group method is designed, by which the sequence information is smoothly sent to the multi-level local information layers for feature exaction. Experimental results show that, on two open session datasets, the performance of DHF-Net is improved by 1.8% and 1.2%, respectively. |
Tomašević, Darian; Peer, Peter; Štruc, Vitomir BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images Inproceedings In: IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) , pp. 1-10, 2022. @inproceedings{TomasevicIJCBBiOcular,
title = {BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images},
author = {Darian Tomašević and Peter Peer and Vitomir Štruc },
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/12/BiModal_StyleGAN.pdf
https://arxiv.org/pdf/2205.01536.pdf},
year = {2022},
date = {2022-10-20},
urldate = {2022-10-20},
booktitle = {IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) },
pages = {1-10},
abstract = {Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at: https://github.com/dariant/BiOcularGAN.},
keywords = {biometrics, CNN, data synthesis, deep learning, ocular, segmentation, StyleGAN, synthetic data},
pubstate = {published},
tppubtype = {inproceedings}
}
Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at: https://github.com/dariant/BiOcularGAN. |
Vitek, Matej; Das, Abhijit; Lucio, Diego Rafael; Jr., Luiz Antonio Zanlorensi; Menotti, David; Khiarak, Jalil Nourmohammadi; Shahpar, Mohsen Akbari; Asgari-Chenaghlu, Meysam; Jaryani, Farhang; Tapia, Juan E.; Valenzuela, Andres; Wang, Caiyong; Wang, Yunlong; He, Zhaofeng; Sun, Zhenan; Boutros, Fadi; Damer, Naser; Grebe, Jonas Henry; Kuijper, Arjan; Raja, Kiran; Gupta, Gourav; Zampoukis, Georgios; Tsochatzidis, Lazaros; Pratikakis, Ioannis; Kumar, S. V. Aruna; Harish, B. S.; Pal, Umapada; Peer, Peter; Štruc, Vitomir Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach Journal Article In: IEEE Transactions on Information Forensics and Security, 2022, ISSN: 1556-6013. @article{TIFS_Sclera2022,
title = {Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach},
author = {Matej Vitek and Abhijit Das and Diego Rafael Lucio and Luiz Antonio Zanlorensi Jr. and David Menotti and Jalil Nourmohammadi Khiarak and Mohsen Akbari Shahpar and Meysam Asgari-Chenaghlu and Farhang Jaryani and Juan E. Tapia and Andres Valenzuela and Caiyong Wang and Yunlong Wang and Zhaofeng He and Zhenan Sun and Fadi Boutros and Naser Damer and Jonas Henry Grebe and Arjan Kuijper and Kiran Raja and Gourav Gupta and Georgios Zampoukis and Lazaros Tsochatzidis and Ioannis Pratikakis and S. V. Aruna Kumar and B. S. Harish and Umapada Pal and Peter Peer and Vitomir Štruc},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926136},
doi = {10.1109/TIFS.2022.3216468},
issn = {1556-6013},
year = {2022},
date = {2022-10-18},
urldate = {2022-10-18},
journal = {IEEE Transactions on Information Forensics and Security},
abstract = {Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.},
keywords = {bias, biometrics, fairness, group evaluation, ocular, sclera, sclera segmentation, segmentation},
pubstate = {published},
tppubtype = {article}
}
Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias. |
Ivanovska, Marija; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir; Batagelj, Borut Face Morphing Attack Detection Using Privacy-Aware Training Data Inproceedings In: Proceedings of ERK 2022, pp. 1-4, 2022. @inproceedings{MarijaMorphing,
title = {Face Morphing Attack Detection Using Privacy-Aware Training Data},
author = {Marija Ivanovska and Andrej Kronovšek and Peter Peer and Vitomir Štruc and Borut Batagelj },
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/08/2022_ERK__Face_Morphing_Attack_Detecton_Using_Privacy_Aware_Training_Data.pdf},
year = {2022},
date = {2022-08-01},
urldate = {2022-08-01},
booktitle = {Proceedings of ERK 2022},
pages = {1-4},
abstract = {Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios.},
keywords = {competition, face, face morphing, face morphing attack, face morphing detection, private data, synthetic data},
pubstate = {published},
tppubtype = {inproceedings}
}
Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios. |
Šircelj, Jaka; Peer, Peter; Solina, Franc; Štruc, Vitomir Hierarchical Superquadric Decomposition with Implicit Space Separation Inproceedings In: Proceedings of ERK 2022, pp. 1-4, 2022. @inproceedings{SirceljSuperQuadrics,
title = {Hierarchical Superquadric Decomposition with Implicit Space Separation},
author = {Jaka Šircelj and Peter Peer and Franc Solina and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/08/sq_erk.pdf},
year = {2022},
date = {2022-08-01},
urldate = {2022-08-01},
booktitle = {Proceedings of ERK 2022},
pages = {1-4},
abstract = {We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of superquadrics recovering finer and finer details. While such hierarchical methods have been studied before, we introduce a new way of splitting the object space using only properties of the predicted superquadrics. The method is trained and evaluated on the ShapeNet dataset. The results of our experiments suggest that reasonable reconstructions can be obtained with the proposed approach for a diverse set of objects with complex geometry.},
keywords = {CNN, deep learning, depth estimation, iterative procedure, model fitting, recursive model, superquadric, superquadrics, volumetric primitive},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of superquadrics recovering finer and finer details. While such hierarchical methods have been studied before, we introduce a new way of splitting the object space using only properties of the predicted superquadrics. The method is trained and evaluated on the ShapeNet dataset. The results of our experiments suggest that reasonable reconstructions can be obtained with the proposed approach for a diverse set of objects with complex geometry. |
Grm, Klemen; Štruc, Vitomir Optimization-based Image Filter Design for Self-supervised Super-resolution Training Inproceedings In: Proceedings of ERK 2022, 2022. @inproceedings{Grm2022Erk,
title = {Optimization-based Image Filter Design for Self-supervised Super-resolution Training},
author = {Klemen Grm and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/08/erk22_filtri.pdf},
year = {2022},
date = {2022-08-01},
booktitle = {Proceedings of ERK 2022},
abstract = {Single-image super-resolution can be posed as a self - supervised machine learning task, where the training inputs and targets are derived from an unlabelled dataset of high-resolution images. For super-resolution training, the derivation takes the form of a degradation function that yields low-resolution images given high-resolution ones. Typically, the degradation function is selected manually based on heuristics, such as the desired magnification ratio of the super-resolution method being trained. In this paper, we instead propose principled, optimization-based methods for picking the image filter of the degradation function based on its desired properties in the frequency domain. We develop implicit and explicit methods for filter optimization and demonstrate the resulting filters are better at rejecting aliasing and matching the frequency domain characteristics of real-life low-resolution images than commonly used heuristic picks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Single-image super-resolution can be posed as a self - supervised machine learning task, where the training inputs and targets are derived from an unlabelled dataset of high-resolution images. For super-resolution training, the derivation takes the form of a degradation function that yields low-resolution images given high-resolution ones. Typically, the degradation function is selected manually based on heuristics, such as the desired magnification ratio of the super-resolution method being trained. In this paper, we instead propose principled, optimization-based methods for picking the image filter of the degradation function based on its desired properties in the frequency domain. We develop implicit and explicit methods for filter optimization and demonstrate the resulting filters are better at rejecting aliasing and matching the frequency domain characteristics of real-life low-resolution images than commonly used heuristic picks. |
Babnik, Žiga; Štruc, Vitomir Iterativna optimizacija ocen kakovosti slikovnih podatkov v sistemih za razpoznavanje obrazov Inproceedings In: Proceedings of ERK 2022, pp. 1-4, 2022. @inproceedings{BabnikErk2022,
title = {Iterativna optimizacija ocen kakovosti slikovnih podatkov v sistemih za razpoznavanje obrazov},
author = {Žiga Babnik and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/08/ERK_2022.pdf},
year = {2022},
date = {2022-08-01},
booktitle = {Proceedings of ERK 2022},
pages = {1-4},
abstract = {While recent face recognition (FR) systems achieve excellent results in many deployment scenarios, their performance in challenging real-world settings is still under question. For this reason, face image quality assessment (FIQA) techniques aim to support FR systems, by providing them with sample quality information that can be used to reject poor quality data unsuitable for recognition purposes. Several groups of FIQA methods relying on different concepts have been proposed in the literature, all of which can be used for generating quality scores of facial images that can serve as pseudo ground-truth (quality) labels and be exploited for training (regression-based) quality estimation models. Several FIQA approaches show that a significant amount of sample-quality information can be extracted from mated similarity-score distributions generated with some face matcher. Based on this insight, we propose in this paper a quality label optimization approach, which incorporates sample-quality information from mated-pair similarities into quality predictions of existing off-the-shelf FIQA techniques. We evaluate the proposed approach using three state-of-the-art FIQA methods over three diverse datasets. The results of our experiments show that the proposed optimization procedure heavily depends on the number of executed optimization iterations. At ten iterations, the approach seems to perform the best, consistently outperforming the base quality scores of the three FIQA methods, chosen for the experiments.},
keywords = {CNN, face image quality estimation, face quality, face recognition, optimization, supervised quality estimation},
pubstate = {published},
tppubtype = {inproceedings}
}
While recent face recognition (FR) systems achieve excellent results in many deployment scenarios, their performance in challenging real-world settings is still under question. For this reason, face image quality assessment (FIQA) techniques aim to support FR systems, by providing them with sample quality information that can be used to reject poor quality data unsuitable for recognition purposes. Several groups of FIQA methods relying on different concepts have been proposed in the literature, all of which can be used for generating quality scores of facial images that can serve as pseudo ground-truth (quality) labels and be exploited for training (regression-based) quality estimation models. Several FIQA approaches show that a significant amount of sample-quality information can be extracted from mated similarity-score distributions generated with some face matcher. Based on this insight, we propose in this paper a quality label optimization approach, which incorporates sample-quality information from mated-pair similarities into quality predictions of existing off-the-shelf FIQA techniques. We evaluate the proposed approach using three state-of-the-art FIQA methods over three diverse datasets. The results of our experiments show that the proposed optimization procedure heavily depends on the number of executed optimization iterations. At ten iterations, the approach seems to perform the best, consistently outperforming the base quality scores of the three FIQA methods, chosen for the experiments. |
Tomašecić, Darian; Peer, Peter; Solina, Franc; Jaklič, Aleš; Štruc, Vitomir Reconstructing Superquadrics from Intensity and Color Images Journal Article In: Sensors, vol. 22, iss. 4, no. 5332, 2022. @article{TomasevicSensors,
title = {Reconstructing Superquadrics from Intensity and Color Images},
author = {Darian Tomašecić and Peter Peer and Franc Solina and Aleš Jaklič and Vitomir Štruc},
url = {https://www.mdpi.com/1424-8220/22/14/5332/pdf?version=1658380987},
doi = {https://doi.org/10.3390/s22145332},
year = {2022},
date = {2022-07-16},
journal = {Sensors},
volume = {22},
number = {5332},
issue = {4},
abstract = {The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Recent research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. In this paper, we extended these reconstruction methods to intensity and color images. Specifically, we used a dedicated convolutional neural network (CNN) model to reconstruct a single superquadric from the given input image. We analyzed the results in a qualitative and quantitative manner, by visualizing reconstructed superquadrics as well as observing error and accuracy distributions of predictions. We showed that a CNN model designed around a simple ResNet backbone can be used to accurately reconstruct superquadrics from images containing one object, but only if one of the spatial parameters is fixed or if it can be determined from other image characteristics, e.g., shadows. Furthermore, we experimented with images of increasing complexity, for example, by adding textures, and observed that the results degraded only slightly. In addition, we show that our model outperforms the current state-of-the-art method on the studied task. Our final result is a highly accurate superquadric reconstruction model, which can also reconstruct superquadrics from real images of simple objects, without additional training.},
keywords = {arrs, CNN, depth data, depth estimation, depth sensing, intensity images, superquadric, superquadrics},
pubstate = {published},
tppubtype = {article}
}
The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Recent research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. In this paper, we extended these reconstruction methods to intensity and color images. Specifically, we used a dedicated convolutional neural network (CNN) model to reconstruct a single superquadric from the given input image. We analyzed the results in a qualitative and quantitative manner, by visualizing reconstructed superquadrics as well as observing error and accuracy distributions of predictions. We showed that a CNN model designed around a simple ResNet backbone can be used to accurately reconstruct superquadrics from images containing one object, but only if one of the spatial parameters is fixed or if it can be determined from other image characteristics, e.g., shadows. Furthermore, we experimented with images of increasing complexity, for example, by adding textures, and observed that the results degraded only slightly. In addition, we show that our model outperforms the current state-of-the-art method on the studied task. Our final result is a highly accurate superquadric reconstruction model, which can also reconstruct superquadrics from real images of simple objects, without additional training. |
Babnik, Žiga; Peer, Peter; Štruc, Vitomir FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration Inproceedings In: IAPR International Conference on Pattern Recognition (ICPR), 2022. @inproceedings{ICPR2022,
title = {FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration},
author = {Žiga Babnik and Peter Peer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/06/ICPR_2022___paper-17.pdf},
year = {2022},
date = {2022-05-17},
urldate = {2022-05-17},
booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},
abstract = {Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent. As such, the proposed approach is the first to link image quality to adversarial attacks. Comprehensive (cross-model as well as model-specific) experiments are conducted with four benchmark datasets, i.e., LFW, CFP–FP, XQLFW and IJB–C, four FR models, i.e., CosFace, ArcFace, CurricularFace and ElasticFace and in comparison to seven state-of-the-art FIQA methods to demonstrate the performance of FaceQAN. Experimental results show that FaceQAN achieves competitive results, while exhibiting several desirable characteristics. The source code for FaceQAN will be made publicly available.},
keywords = {adversarial examples, adversarial noise, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent. As such, the proposed approach is the first to link image quality to adversarial attacks. Comprehensive (cross-model as well as model-specific) experiments are conducted with four benchmark datasets, i.e., LFW, CFP–FP, XQLFW and IJB–C, four FR models, i.e., CosFace, ArcFace, CurricularFace and ElasticFace and in comparison to seven state-of-the-art FIQA methods to demonstrate the performance of FaceQAN. Experimental results show that FaceQAN achieves competitive results, while exhibiting several desirable characteristics. The source code for FaceQAN will be made publicly available. |
Babnik, Žiga; Štruc, Vitomir Assessing Bias in Face Image Quality Assessment Inproceedings In: EUSIPCO 2022, 2022. @inproceedings{EUSIPCO_2022,
title = {Assessing Bias in Face Image Quality Assessment},
author = {Žiga Babnik and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/06/EUSIPCO_2022___paper.pdf},
year = {2022},
date = {2022-05-16},
urldate = {2022-05-16},
booktitle = {EUSIPCO 2022},
abstract = {Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality.
Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investigate the demographic biases associated with FIQA approaches, this paper presents a comprehensive study involving a variety of quality assessment methods (general-purpose image quality assessment, supervised face quality assessment, and unsupervised face quality assessment methods) and three diverse state-of-the-art FR models.
Our analysis on the Balanced Faces in the Wild (BFW) dataset shows that all techniques considered are affected more by variations in race than sex. While the general-purpose image quality assessment methods appear to be less biased with respect to the two demographic factors considered, the supervised and unsupervised face image quality assessment methods both show strong bias with a tendency to favor white individuals (of either sex). In addition, we found that methods that are less racially biased perform worse overall. This suggests that the observed bias in FIQA methods is to a significant extent related to the underlying face recognition system.},
keywords = {bias, bias analysis, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment},
pubstate = {published},
tppubtype = {inproceedings}
}
Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality.
Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investigate the demographic biases associated with FIQA approaches, this paper presents a comprehensive study involving a variety of quality assessment methods (general-purpose image quality assessment, supervised face quality assessment, and unsupervised face quality assessment methods) and three diverse state-of-the-art FR models.
Our analysis on the Balanced Faces in the Wild (BFW) dataset shows that all techniques considered are affected more by variations in race than sex. While the general-purpose image quality assessment methods appear to be less biased with respect to the two demographic factors considered, the supervised and unsupervised face image quality assessment methods both show strong bias with a tendency to favor white individuals (of either sex). In addition, we found that methods that are less racially biased perform worse overall. This suggests that the observed bias in FIQA methods is to a significant extent related to the underlying face recognition system. |
Osorio-Roig, Daile; Rathgeb, Christian; Drozdowski, Pawel; Terhörst, Philipp; Štruc, Vitomir; Busch, Christoph An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement Journal Article In: IEEE Transactions on Biometrics, Identity and Behavior (TBIOM), 2022. @article{TBIOM_2022,
title = {An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement},
author = {Daile Osorio-Roig and Christian Rathgeb and Pawel Drozdowski and Philipp Terhörst and Vitomir Štruc and Christoph Busch},
url = {https://arxiv.org/pdf/2111.12405.pdf},
year = {2022},
date = {2022-05-02},
journal = {IEEE Transactions on Biometrics, Identity and Behavior (TBIOM)},
abstract = {In the recent past, different researchers have proposed novel privacy-enhancing face recognition systems designed to conceal soft-biometric information at feature level. These works have reported impressive results, but usually do not consider specific attacks in their analysis of privacy protection. In most cases, the privacy protection capabilities of these schemes are tested through simple machine learning-based classifiers and visualisations of dimensionality reduction tools. In this work, we introduce an attack on feature level-based facial soft–biometric privacy-enhancement techniques. The attack is based on two observations: (1) to achieve high recognition accuracy, certain similarities between facial representations have to be retained in their privacy-enhanced versions; (2) highly similar facial representations usually originate from face images with similar soft-biometric attributes. Based on these observations, the proposed attack compares a privacy-enhanced face representation against a set of privacy-enhanced face representations with known soft-biometric attributes. Subsequently, the best obtained similarity scores are analysed to infer the unknown soft-biometric attributes of the attacked privacy-enhanced face representation. That is, the attack only requires a relatively small database of arbitrary face images and the privacy-enhancing face recognition algorithm as a black-box. In the experiments, the attack is applied to two representative approaches which have previously been reported to reliably conceal the gender in privacy-enhanced face representations. It is shown that the presented attack is able to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90% for both of the analysed privacy-enhancing face recognition systems. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on privacy protection.},
keywords = {attack, face recognition, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy},
pubstate = {published},
tppubtype = {article}
}
In the recent past, different researchers have proposed novel privacy-enhancing face recognition systems designed to conceal soft-biometric information at feature level. These works have reported impressive results, but usually do not consider specific attacks in their analysis of privacy protection. In most cases, the privacy protection capabilities of these schemes are tested through simple machine learning-based classifiers and visualisations of dimensionality reduction tools. In this work, we introduce an attack on feature level-based facial soft–biometric privacy-enhancement techniques. The attack is based on two observations: (1) to achieve high recognition accuracy, certain similarities between facial representations have to be retained in their privacy-enhanced versions; (2) highly similar facial representations usually originate from face images with similar soft-biometric attributes. Based on these observations, the proposed attack compares a privacy-enhanced face representation against a set of privacy-enhanced face representations with known soft-biometric attributes. Subsequently, the best obtained similarity scores are analysed to infer the unknown soft-biometric attributes of the attacked privacy-enhanced face representation. That is, the attack only requires a relatively small database of arbitrary face images and the privacy-enhancing face recognition algorithm as a black-box. In the experiments, the attack is applied to two representative approaches which have previously been reported to reliably conceal the gender in privacy-enhanced face representations. It is shown that the presented attack is able to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90% for both of the analysed privacy-enhancing face recognition systems. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on privacy protection. |
Dvoršak, Grega; Dwivedi, Ankita; Štruc, Vitomir; Peer, Peter; Emeršič, Žiga Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models Inproceedings In: International Workshop on Biometrics and Forensics (IWBF), pp. 1–6, 2022. @inproceedings{KinEars,
title = {Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models},
author = {Grega Dvoršak and Ankita Dwivedi and Vitomir Štruc and Peter Peer and Žiga Emeršič},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/03/Gregovi_Uhlji_Template-2.pdf},
year = {2022},
date = {2022-04-21},
urldate = {2022-04-21},
booktitle = {International Workshop on Biometrics and Forensics (IWBF)},
pages = {1--6},
abstract = {The analysis of kin relations from visual data represents a challenging research problem with important real-world applications. However, research in this area has mostly been limited to the analysis of facial images, despite the potential of other physical (human) characteristics for this task. In this paper, we therefore study the problem of kinship verification from ear images and investigate whether salient appearance characteristics, useful for this task, can be extracted from ear data. To facilitate the study, we introduce a novel dataset, called KinEar, that contains data from 19 families with each family member having from 15 to 31 ear images. Using the KinEar data, we conduct experiments using a Siamese training setup and 5 recent deep learning backbones. The results of our experiments suggests that ear images represent a viable alternative to other modalities for kinship verification, as 4 out of 5 considered models reach a performance of over 60% in terms of the Area Under the Receiver Operating Characteristics (ROC-AUC). },
keywords = {biometrics, CNN, deep learning, ear, ear biometrics, kinear, kinship, kinship recognition, transformer},
pubstate = {published},
tppubtype = {inproceedings}
}
The analysis of kin relations from visual data represents a challenging research problem with important real-world applications. However, research in this area has mostly been limited to the analysis of facial images, despite the potential of other physical (human) characteristics for this task. In this paper, we therefore study the problem of kinship verification from ear images and investigate whether salient appearance characteristics, useful for this task, can be extracted from ear data. To facilitate the study, we introduce a novel dataset, called KinEar, that contains data from 19 families with each family member having from 15 to 31 ear images. Using the KinEar data, we conduct experiments using a Siamese training setup and 5 recent deep learning backbones. The results of our experiments suggests that ear images represent a viable alternative to other modalities for kinship verification, as 4 out of 5 considered models reach a performance of over 60% in terms of the Area Under the Receiver Operating Characteristics (ROC-AUC). |
Jug, Julijan; Lampe, Ajda; Peer, Peter; Štruc, Vitomir Segmentacija telesa z uporabo večciljnega učenja Inproceedings In: Proceedings of Rosus 2022, 2022. @inproceedings{Rosus2022,
title = {Segmentacija telesa z uporabo večciljnega učenja},
author = {Julijan Jug and Ajda Lampe and Peter Peer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/03/Rosus2020.pdf},
year = {2022},
date = {2022-03-17},
booktitle = {Proceedings of Rosus 2022},
abstract = {Segmentacija je pomemben del številnih problemov računalniškega vida, ki vključujejo človeške podobe, in je ena ključnih komponent, ki vpliva na uspešnost vseh nadaljnjih nalog. Več predhodnih del je ta problem obravnavalo z uporabo večciljnega modela, ki izkorišča korelacije med različnimi nalogami za izboljšanje uspešnosti segmentacije. Na podlagi uspešnosti takšnih rešitev v tem prispevku predstavljamo nov večciljni model za segmentacijo/razčlenjevanje ljudi, ki vključuje tri naloge, tj. (i) napoved skeletnih točk, (ii) napoved globinske predstavitve poze in (iii) segmentacijo človeškega telesa. Glavna ideja predlaganega modela Segmentacija-Skelet-Globinska predstavitev (ali na kratko iz angleščine SPD) je naučiti se boljšega modela segmentacije z izmenjavo znanja med različnimi, a med seboj povezanimi nalogami. SPD temelji na skupni hrbtenici globoke nevronske mreže, ki se razcepi na tri glave modela, specifične za nalogo, in se uči z uporabo cilja optimizacije za več nalog. Učinkovitost modela je analizirana s strogimi eksperimenti na nizih podatkov LIP in ATR ter v primerjavi z nedavnim (najsodobnejšim) večciljnim modelom segmentacije telesa. Predstavljene so tudi študije ablacije. Naši eksperimentalni rezultati kažejo, da je predlagani večciljni (segmentacijski) model zelo konkurenčen in da uvedba dodatnih nalog prispeva k večji skupni uspešnosti segmentacije.},
keywords = {deepbeauty, računalniški vid, segmentacija},
pubstate = {published},
tppubtype = {inproceedings}
}
Segmentacija je pomemben del številnih problemov računalniškega vida, ki vključujejo človeške podobe, in je ena ključnih komponent, ki vpliva na uspešnost vseh nadaljnjih nalog. Več predhodnih del je ta problem obravnavalo z uporabo večciljnega modela, ki izkorišča korelacije med različnimi nalogami za izboljšanje uspešnosti segmentacije. Na podlagi uspešnosti takšnih rešitev v tem prispevku predstavljamo nov večciljni model za segmentacijo/razčlenjevanje ljudi, ki vključuje tri naloge, tj. (i) napoved skeletnih točk, (ii) napoved globinske predstavitve poze in (iii) segmentacijo človeškega telesa. Glavna ideja predlaganega modela Segmentacija-Skelet-Globinska predstavitev (ali na kratko iz angleščine SPD) je naučiti se boljšega modela segmentacije z izmenjavo znanja med različnimi, a med seboj povezanimi nalogami. SPD temelji na skupni hrbtenici globoke nevronske mreže, ki se razcepi na tri glave modela, specifične za nalogo, in se uči z uporabo cilja optimizacije za več nalog. Učinkovitost modela je analizirana s strogimi eksperimenti na nizih podatkov LIP in ATR ter v primerjavi z nedavnim (najsodobnejšim) večciljnim modelom segmentacije telesa. Predstavljene so tudi študije ablacije. Naši eksperimentalni rezultati kažejo, da je predlagani večciljni (segmentacijski) model zelo konkurenčen in da uvedba dodatnih nalog prispeva k večji skupni uspešnosti segmentacije. |
Križaj, Janez; Dobrišek, Simon; Štruc, Vitomir Making the most of single sensor information : a novel fusion approach for 3D face recognition using region covariance descriptors and Gaussian mixture models Journal Article In: Sensors, iss. 6, no. 2388, pp. 1-26, 2022. @article{KrizajSensors2022,
title = {Making the most of single sensor information : a novel fusion approach for 3D face recognition using region covariance descriptors and Gaussian mixture models},
author = {Janez Križaj and Simon Dobrišek and Vitomir Štruc},
url = {https://www.mdpi.com/1424-8220/22/6/2388},
doi = {10.3390/s22062388},
year = {2022},
date = {2022-03-01},
journal = {Sensors},
number = {2388},
issue = {6},
pages = {1-26},
abstract = {Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results.},
keywords = {3d face, biometrics, face, face analysis, face images, face recognition},
pubstate = {published},
tppubtype = {article}
}
Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results. |
Jug, Julijan; Lampe, Ajda; Štruc, Vitomir; Peer, Peter Body Segmentation Using Multi-task Learning Inproceedings In: International Conference on Artificial Intelligence in Information and Communication (ICAIIC), IEEE, 2022, ISBN: 978-1-6654-5818-4. @inproceedings{JulijanJugBody,
title = {Body Segmentation Using Multi-task Learning},
author = {Julijan Jug and Ajda Lampe and Vitomir Štruc and Peter Peer},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/03/ICAIIC_paper.pdf},
doi = {10.1109/ICAIIC54071.2022.9722662},
isbn = {978-1-6654-5818-4},
year = {2022},
date = {2022-01-20},
urldate = {2022-01-20},
booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC)},
publisher = {IEEE},
abstract = {Body segmentation is an important step in many computer vision problems involving human images and one of the key components that affects the performance of all downstream tasks. Several prior works have approached this problem using a multi-task model that exploits correlations between different tasks to improve segmentation performance. Based on the success of such solutions, we present in this paper a novel multi-task model for human segmentation/parsing that involves three tasks, i.e., (i) keypoint-based skeleton estimation, (ii) dense pose prediction, and (iii) human-body segmentation. The main idea behind the proposed Segmentation--Pose--DensePose model (or SPD for short) is to learn a better segmentation model by sharing knowledge across different, yet related tasks. SPD is based on a shared deep neural network backbone that branches off into three task-specific model heads and is learned using a multi-task optimization objective. The performance of the model is analysed through rigorous experiments on the LIP and ATR datasets and in comparison to a recent (state-of-the-art) multi-task body-segmentation model. Comprehensive ablation studies are also presented. Our experimental results show that the proposed multi-task (segmentation) model is highly competitive and that the introduction of additional tasks contributes towards a higher overall segmentation performance. },
keywords = {body segmentation, cn, CNN, computer vision, deep beauty, deep learning, multi-task learning, segmentation, virtual try-on},
pubstate = {published},
tppubtype = {inproceedings}
}
Body segmentation is an important step in many computer vision problems involving human images and one of the key components that affects the performance of all downstream tasks. Several prior works have approached this problem using a multi-task model that exploits correlations between different tasks to improve segmentation performance. Based on the success of such solutions, we present in this paper a novel multi-task model for human segmentation/parsing that involves three tasks, i.e., (i) keypoint-based skeleton estimation, (ii) dense pose prediction, and (iii) human-body segmentation. The main idea behind the proposed Segmentation--Pose--DensePose model (or SPD for short) is to learn a better segmentation model by sharing knowledge across different, yet related tasks. SPD is based on a shared deep neural network backbone that branches off into three task-specific model heads and is learned using a multi-task optimization objective. The performance of the model is analysed through rigorous experiments on the LIP and ATR datasets and in comparison to a recent (state-of-the-art) multi-task body-segmentation model. Comprehensive ablation studies are also presented. Our experimental results show that the proposed multi-task (segmentation) model is highly competitive and that the introduction of additional tasks contributes towards a higher overall segmentation performance. |
Fele, Benjamin; Lampe, Ajda; Peer, Peter; Štruc, Vitomir C-VTON: Context-Driven Image-Based Virtual Try-On Network Inproceedings In: IEEE/CVF Winter Applications in Computer Vision (WACV), pp. 1–10, 2022. @inproceedings{WACV2022_Fele,
title = {C-VTON: Context-Driven Image-Based Virtual Try-On Network},
author = {Benjamin Fele and Ajda Lampe and Peter Peer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/12/WACV2022_Benjamin_compressed-1.pdf},
year = {2022},
date = {2022-01-04},
urldate = {2022-01-04},
booktitle = {IEEE/CVF Winter Applications in Computer Vision (WACV)},
pages = {1--10},
abstract = {Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art.},
keywords = {computer vision, deepbeauty, fashion, generative models, image editing, try-on, virtual try-on},
pubstate = {published},
tppubtype = {inproceedings}
}
Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art. |
Stoimchev, Marjan; Ivanovska, Marija; Štruc, Vitomir Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition Journal Article In: Sensors, vol. 22, no. 1, pp. 1-26, 2022. @article{Stoimchev2022,
title = {Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition},
author = {Marjan Stoimchev and Marija Ivanovska and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/03/sensors-22-00073_reduced.pdf},
doi = {https://doi.org/10.3390/s22010073},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {1},
pages = {1-26},
abstract = {In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.},
keywords = {biometrics; computer vision; deep learning; palmprints},
pubstate = {published},
tppubtype = {article}
}
In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available. |
Rot, Peter; Peer, Peter; Štruc, Vitomir Detecting Soft-Biometric Privacy Enhancement Incollection In: Rathgeb, Christian; Tolosana, Ruben; Vera-Rodriguez, Ruben; Busch, Christoph (Ed.): Handbook of Digital Face Manipulation and Detection, 2022. @incollection{RotManipulationBook,
title = {Detecting Soft-Biometric Privacy Enhancement},
author = {Peter Rot and Peter Peer and Vitomir Štruc},
editor = {Christian Rathgeb and Ruben Tolosana and Ruben Vera-Rodriguez and Christoph Busch},
url = {https://link.springer.com/chapter/10.1007/978-3-030-87664-7_18},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Handbook of Digital Face Manipulation and Detection},
keywords = {biometrics, face, privacy, privacy enhancement, privacy-enhancing techniques, soft biometric privacy},
pubstate = {published},
tppubtype = {incollection}
}
|
Tolosana, Ruben; Rathgeb, Christian; Vera-Rodriguez, Ruben; Busch, Christoph; Verdilova, Luisa; Lyu, Siwei; Nguyen, Huy H.; Yamagishi, Junichi; Echizen, Isao; Rot, Peter; Grm, Klemen; Štruc, Vitomir; Datcheva, Antitza; Akhtar, Zahid; Romero-Tapiador, Sergio; Fierrez, Julian; Morales, Aythami; Ortega-Garcia, Javier; Kindt, Els; Jasserand, Catherine; Kalvet, Tarmo; Tiits, Marek Future Trends in Digital Face Manipulation and Detection Incollection In: Rathgeb, Christian; Tolosana, Ruben; Vera-Rodriguez, Ruben; Busch, Christoph (Ed.): Handbook of Digital Face Manipulation and Detection, pp. 463–482, 2022, ISBN: 978-3-030-87663-0. @incollection{ManipulationFace2022,
title = {Future Trends in Digital Face Manipulation and Detection},
author = {Ruben Tolosana and Christian Rathgeb and Ruben Vera-Rodriguez and Christoph Busch and Luisa Verdilova and Siwei Lyu and Huy H. Nguyen and Junichi Yamagishi and Isao Echizen and Peter Rot and Klemen Grm and Vitomir Štruc and Antitza Datcheva and Zahid Akhtar and Sergio Romero-Tapiador and Julian Fierrez and Aythami Morales and Javier Ortega-Garcia and Els Kindt and Catherine Jasserand and Tarmo Kalvet and Marek Tiits},
editor = {Christian Rathgeb and Ruben Tolosana and Ruben Vera-Rodriguez and Christoph Busch},
url = {https://link.springer.com/chapter/10.1007/978-3-030-87664-7_21},
doi = {https://doi.org/10.1007/978-3-030-87664-7_21},
isbn = {978-3-030-87663-0},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Handbook of Digital Face Manipulation and Detection},
pages = {463--482},
abstract = {Recently, digital face manipulation and its detection have sparked large interest in industry and academia around the world. Numerous approaches have been proposed in the literature to create realistic face manipulations, such as DeepFakes and face morphs. To the human eye manipulated images and videos can be almost indistinguishable from real content. Although impressive progress has been reported in the automatic detection of such face manipulations, this research field is often considered to be a cat and mouse game. This chapter briefly discusses the state of the art of digital face manipulation and detection. Issues and challenges that need to be tackled by the research community are summarized, along with future trends in the field.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Recently, digital face manipulation and its detection have sparked large interest in industry and academia around the world. Numerous approaches have been proposed in the literature to create realistic face manipulations, such as DeepFakes and face morphs. To the human eye manipulated images and videos can be almost indistinguishable from real content. Although impressive progress has been reported in the automatic detection of such face manipulations, this research field is often considered to be a cat and mouse game. This chapter briefly discusses the state of the art of digital face manipulation and detection. Issues and challenges that need to be tackled by the research community are summarized, along with future trends in the field. |
2021
|
Emeršič, Žiga; Sušanj, Diego; Meden, Blaž; Peer, Peter; Štruc, Vitomir ContexedNet : Context-Aware Ear Detection in Unconstrained Settings Journal Article In: IEEE Access, pp. 1–17, 2021, ISSN: 2169-3536. @article{ContexedNet_Emersic_2021,
title = {ContexedNet : Context-Aware Ear Detection in Unconstrained Settings},
author = {Žiga Emeršič and Diego Sušanj and Blaž Meden and Peter Peer and Vitomir Štruc},
editor = {ContexedNet : Context-Aware Ear Detection in Unconstrained Settings},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9583244},
issn = {2169-3536},
year = {2021},
date = {2021-10-20},
urldate = {2021-10-20},
journal = {IEEE Access},
pages = {1--17},
abstract = {Ear detection represents one of the key components of contemporary ear recognition systems. While significant progress has been made in the area of ear detection over recent years, most of the improvements are direct results of advances in the field of visual object detection. Only a limited number of techniques presented in the literature are domain--specific and designed explicitly with ear detection in mind. In this paper, we aim to address this gap and present a novel detection approach that does not rely only on general ear (object) appearance, but also exploits contextual information, i.e., face--part locations, to ensure accurate and robust ear detection with images captured in a wide variety of imaging conditions. The proposed approach is based on a Context--aware Ear Detection Network (ContexedNet) and poses ear detection as a semantic image segmentation problem. ContexedNet consists of two processing paths: 1) a context--provider that extracts probability maps corresponding to the locations of facial parts from the input image, and 2) a dedicated ear segmentation model that integrates the computed probability maps into a context--aware segmentation-based ear detection procedure. ContexedNet is evaluated in rigorous experiments on the AWE and UBEAR datasets and shown to ensure competitive performance when evaluated against state--of--the--art ear detection models from the literature. Additionally, because the proposed contextualization is model agnostic, it can also be utilized with other ear detection techniques to improve performance.},
keywords = {biometrics, contextual information, deep leraning, ear detection, ear recognition, ear segmentation, neural networks, segmentation},
pubstate = {published},
tppubtype = {article}
}
Ear detection represents one of the key components of contemporary ear recognition systems. While significant progress has been made in the area of ear detection over recent years, most of the improvements are direct results of advances in the field of visual object detection. Only a limited number of techniques presented in the literature are domain--specific and designed explicitly with ear detection in mind. In this paper, we aim to address this gap and present a novel detection approach that does not rely only on general ear (object) appearance, but also exploits contextual information, i.e., face--part locations, to ensure accurate and robust ear detection with images captured in a wide variety of imaging conditions. The proposed approach is based on a Context--aware Ear Detection Network (ContexedNet) and poses ear detection as a semantic image segmentation problem. ContexedNet consists of two processing paths: 1) a context--provider that extracts probability maps corresponding to the locations of facial parts from the input image, and 2) a dedicated ear segmentation model that integrates the computed probability maps into a context--aware segmentation-based ear detection procedure. ContexedNet is evaluated in rigorous experiments on the AWE and UBEAR datasets and shown to ensure competitive performance when evaluated against state--of--the--art ear detection models from the literature. Additionally, because the proposed contextualization is model agnostic, it can also be utilized with other ear detection techniques to improve performance. |
Ivanovska, Marija; Štruc, Vitomir A Comparative Study on Discriminative and One--Class Learning Models for Deepfake Detection Inproceedings In: Proceedings of ERK 2021, pp. 1–4, 2021. @inproceedings{ERK_Marija_2021,
title = {A Comparative Study on Discriminative and One--Class Learning Models for Deepfake Detection},
author = {Marija Ivanovska and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2021/10/ERK_2021__A_Comparative_Study_of_Discriminative_and_One__Class_Learning_Models_for_Deepfake_Detection.pdf},
year = {2021},
date = {2021-09-20},
booktitle = {Proceedings of ERK 2021},
pages = {1--4},
abstract = {Deepfakes or manipulated face images, where a donor's face is swapped with the face of a target person, have gained enormous popularity among the general public recently. With the advancements in artificial intelligence and generative modeling
such images can nowadays be easily generated and used to spread misinformation and harm individuals, businesses or society. As the tools for generating deepfakes are rapidly improving, it is critical for deepfake detection models to be able to recognize advanced, sophisticated data manipulations, including those that have not been seen during training. In this paper, we explore the use of one--class learning models as an alternative to discriminative methods for the detection of deepfakes. We conduct a comparative study with three popular deepfake datasets and investigate the performance of selected (discriminative and one-class) detection models in matched- and cross-dataset experiments. Our results show that disciminative models significantly outperform one-class models when training and testing data come from the same dataset, but degrade considerably when the characteristics of the testing data deviate from the training setting. In such cases, one-class models tend to generalize much better.},
keywords = {biometrics, comparative study, computer vision, deepfake detection, deepfakes, detection, face, one-class learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Deepfakes or manipulated face images, where a donor's face is swapped with the face of a target person, have gained enormous popularity among the general public recently. With the advancements in artificial intelligence and generative modeling
such images can nowadays be easily generated and used to spread misinformation and harm individuals, businesses or society. As the tools for generating deepfakes are rapidly improving, it is critical for deepfake detection models to be able to recognize advanced, sophisticated data manipulations, including those that have not been seen during training. In this paper, we explore the use of one--class learning models as an alternative to discriminative methods for the detection of deepfakes. We conduct a comparative study with three popular deepfake datasets and investigate the performance of selected (discriminative and one-class) detection models in matched- and cross-dataset experiments. Our results show that disciminative models significantly outperform one-class models when training and testing data come from the same dataset, but degrade considerably when the characteristics of the testing data deviate from the training setting. In such cases, one-class models tend to generalize much better. |
Grm, Klemen; Vitomir, Štruc Frequency Band Encoding for Face Super-Resolution Inproceedings In: Proceedings of ERK 2021, pp. 1-4, 2021. @inproceedings{Grm-SuperResolution_ERK2021,
title = {Frequency Band Encoding for Face Super-Resolution},
author = {Klemen Grm and Štruc Vitomir},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2021/10/SRAE_ERK21.pdf},
year = {2021},
date = {2021-09-10},
booktitle = {Proceedings of ERK 2021},
pages = {1-4},
abstract = {In this paper, we present a novel method for face super-resolution based on an encoder-decoder architecture. Unlike previous approaches, which focused primarily on directly reconstructing the high-resolution face appearance from low-resolution images, our method relies on a multi-stage approach where we learn a face representation in different frequency bands, followed by decoding the representation into a high-resolution image. Using quantitative experiments, we are able to demonstrate that this approach results in better face image reconstruction, as well as aiding in downstream semantic tasks such as face recognition and face verification.},
keywords = {CNN, deep learning, face, face hallucination, frequency encoding, super-resolution},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present a novel method for face super-resolution based on an encoder-decoder architecture. Unlike previous approaches, which focused primarily on directly reconstructing the high-resolution face appearance from low-resolution images, our method relies on a multi-stage approach where we learn a face representation in different frequency bands, followed by decoding the representation into a high-resolution image. Using quantitative experiments, we are able to demonstrate that this approach results in better face image reconstruction, as well as aiding in downstream semantic tasks such as face recognition and face verification. |
Boutros, Fadi; Damer, Naser; Kolf, Jan Niklas; Raja, Kiran; Kirchbuchner, Florian; Ramachandra, Raghavendra; Kuijper, Arjan; Fang, Pengcheng; Zhang, Chao; Wang, Fei; Montero, David; Aginako, Naiara; Sierra, Basilio; Nieto, Marcos; Erakin, Mustafa Ekrem; Demir, Ugur; Ekenel, Hazım Kemal; Kataoka, Asaki; Ichikawa, Kohei; Kubo, Shizuma; Zhang, Jie; He, Mingjie; Han, Dan; Shan, Shiguang; Grm, Klemen; Štruc, Vitomir; Seneviratne, Sachith; Kasthuriarachchi, Nuran; Rasnayaka, Sanka; Neto, Pedro C.; Sequeira, Ana F.; Pinto, Joao Ribeiro; Saffari, Mohsen; Cardoso, Jaime S. MFR 2021: Masked Face Recognition Competition Inproceedings In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. @inproceedings{MFR_IJCB2021,
title = {MFR 2021: Masked Face Recognition Competition},
author = {Fadi Boutros and Naser Damer and Jan Niklas Kolf and Kiran Raja and Florian Kirchbuchner and Raghavendra Ramachandra and Arjan Kuijper and Pengcheng Fang and Chao Zhang and Fei Wang and David Montero and Naiara Aginako and Basilio Sierra and Marcos Nieto and Mustafa Ekrem Erakin and Ugur Demir and Hazım Kemal Ekenel and Asaki Kataoka and Kohei Ichikawa and Shizuma Kubo and Jie Zhang and Mingjie He and Dan Han and Shiguang Shan and Klemen Grm and Vitomir Štruc and Sachith Seneviratne and Nuran Kasthuriarachchi and Sanka Rasnayaka and Pedro C. Neto and Ana F. Sequeira and Joao Ribeiro Pinto and Mohsen Saffari and Jaime S. Cardoso},
url = {https://ieeexplore.ieee.org/iel7/9484326/9484328/09484337.pdf?casa_token=OOL4s274P0YAAAAA:XE7ga2rP_wNom2Zeva75ZwNwN-HKz6kF1HZtkpzrdTdz36eaGcLffWkzOgIe3xU2PqaU30qTLws},
doi = {10.1109/IJCB52358.2021.9484337},
year = {2021},
date = {2021-08-01},
booktitle = {Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021)},
abstract = {This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.
},
keywords = {biometrics, face recognition, masks},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.
|
Wang, Caiyong; Wang, Yunlong; Zhang, Kunbo; Muhammad, Jawad; Lu, Tianhao; Zhang, Qi; Tian, Qichuan; He, Zhaofeng; Sun, Zhenan; Zhang, Yiwen; Liu, Tianbao; Yang, Wei; Wu, Dongliang; Liu, Yingfeng; Zhou, Ruiye; Wu, Huihai; Zhang, Hao; Wang, Junbao; Wang, Jiayi; Xiong, Wantong; Shi, Xueyu; Zeng, Shao; Li, Peihua; Sun, Haodong; Wang, Jing; Zhang, Jiale; Wang, Qi; Wu, Huijie; Zhang, Xinhui; Li, Haiqing; Chen, Yu; Chen, Liang; Zhang, Menghan; Sun, Ye; Zhou, Zhiyong; Boutros, Fadi; Damer, Naser; Kuijper, Arjan; Tapia, Juan; Valenzuela, Andres; Busch, Christoph; Gupta, Gourav; Raja, Kiran; Wu, Xi; Li, Xiaojie; Yang, Jingfu; Jing, Hongyan; Wang, Xin; Kong, Bin; Yin, Youbing; Song, Qi; Lyu, Siwei; Hu, Shu; Premk, Leon; Vitek, Matej; Štruc, Vitomir; Peer, Peter; Khiarak, Jalil Nourmohammadi; Jaryani, Farhang; Nasab, Samaneh Salehi; Moafinejad, Seyed Naeim; Amini, Yasin; Noshad, Morteza NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization Inproceedings In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. @inproceedings{NIR_IJCB2021,
title = {NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization},
author = {Caiyong Wang and Yunlong Wang and Kunbo Zhang and Jawad Muhammad and Tianhao Lu and Qi Zhang and Qichuan Tian and Zhaofeng He and Zhenan Sun and Yiwen Zhang and Tianbao Liu and Wei Yang and Dongliang Wu and Yingfeng Liu and Ruiye Zhou and Huihai Wu and Hao Zhang and Junbao Wang and Jiayi Wang and Wantong Xiong and Xueyu Shi and Shao Zeng and Peihua Li and Haodong Sun and Jing Wang and Jiale Zhang and Qi Wang and Huijie Wu and Xinhui Zhang and Haiqing Li and Yu Chen and Liang Chen and Menghan Zhang and Ye Sun and Zhiyong Zhou and Fadi Boutros and Naser Damer and Arjan Kuijper and Juan Tapia and Andres Valenzuela and Christoph Busch and Gourav Gupta and Kiran Raja and Xi Wu and Xiaojie Li and Jingfu Yang and Hongyan Jing and Xin Wang and Bin Kong and Youbing Yin and Qi Song and Siwei Lyu and Shu Hu and Leon Premk and Matej Vitek and Vitomir Štruc and Peter Peer and Jalil Nourmohammadi Khiarak and Farhang Jaryani and Samaneh Salehi Nasab and Seyed Naeim Moafinejad and Yasin Amini and Morteza Noshad},
url = {https://ieeexplore.ieee.org/iel7/9484326/9484328/09484336.pdf?casa_token=FOKx4ltO-hYAAAAA:dCkNHfumDzPGkAipRdbppNWpzAiUYUrJL6OrAjNmimTxUA0Vmx311-3-J3ej7YQc_zONxEO-XKo},
doi = {10.1109/IJCB52358.2021.9484336},
year = {2021},
date = {2021-08-01},
booktitle = {Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021)},
abstract = {For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.},
keywords = {biometrics, competition, iris, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research. |
Peter Rot Blaz Meden, Philipp Terhorst Privacy-Enhancing Face Biometrics: A Comprehensive Survey Journal Article In: IEEE Transactions on Information Forensics and Security, vol. 16, pp. 4147-4183, 2021. @article{TIFS_PrivacySurveyb,
title = {Privacy-Enhancing Face Biometrics: A Comprehensive Survey},
author = {Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Struc},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9481149
https://lmi.fe.uni-lj.si/en/visual_privacy_of_faces__a_survey_preprint-compressed/},
doi = {10.1109/TIFS.2021.3096024},
year = {2021},
date = {2021-07-12},
journal = {IEEE Transactions on Information Forensics and Security},
volume = {16},
pages = {4147-4183},
abstract = {Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy--enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy--related research in the area of biometrics and review existing work on textit{Biometric Privacy--Enhancing Techniques} (B--PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B--PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future. },
keywords = {biometrics, deidentification, face analysis, face deidentification, face recognition, face verification, FaceGEN, privacy, privacy protection, privacy-enhancing techniques, soft biometric privacy},
pubstate = {published},
tppubtype = {article}
}
Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy--enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy--related research in the area of biometrics and review existing work on textit{Biometric Privacy--Enhancing Techniques} (B--PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B--PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future. |
Pevec, Klemen; Grm, Klemen; Štruc, Vitomir Benchmarking Crowd-Counting Techniques across Image Characteristics Journal Article In: Elektorethniski Vestnik, vol. 88, iss. 5, pp. 227-235, 2021. @article{CrowdCountingPevec,
title = {Benchmarking Crowd-Counting Techniques across Image Characteristics},
author = {Klemen Pevec and Klemen Grm and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/10/Pevec.pdf
https://ev.fe.uni-lj.si/5-2021/Pevec.pdf},
year = {2021},
date = {2021-05-01},
journal = {Elektorethniski Vestnik},
volume = {88},
issue = {5},
pages = {227-235},
abstract = {Crowd--counting is a longstanding computer vision used in estimating the crowd sizes for security purposes at public protests in streets, public gatherings, for collecting crowd statistics at airports, malls, concerts, conferences, and other similar venues, and for monitoring people and crowds during public health crises (such as the one caused by COVID-19). Recently, the performance of automated methods for crowd--counting from single images has improved particularly due to the introduction of deep learning techniques and large labelled training datasets. However, the robustness of these methods to varying imaging conditions, such as weather, image perspective, and large variations in the crowd size has not been studied in-depth in the open literature. To address this gap, a systematic study on the robustness of four recently developed crowd--counting methods is performed in this paper to evaluate their performance with respect to variable (real-life) imaging scenarios that include different event types, weather conditions, image sources and crowd sizes. It is shown that the performance of the tested techniques is degraded in unclear weather conditions (i.e., fog, rain, snow) and also on images taken from large distances by drones. On the opposite, clear weather conditions, crowd--counting methods can provide accurate and usable results.},
keywords = {CNN, crowd counting, drones, image characteristics, model comparison, neural networks},
pubstate = {published},
tppubtype = {article}
}
Crowd--counting is a longstanding computer vision used in estimating the crowd sizes for security purposes at public protests in streets, public gatherings, for collecting crowd statistics at airports, malls, concerts, conferences, and other similar venues, and for monitoring people and crowds during public health crises (such as the one caused by COVID-19). Recently, the performance of automated methods for crowd--counting from single images has improved particularly due to the introduction of deep learning techniques and large labelled training datasets. However, the robustness of these methods to varying imaging conditions, such as weather, image perspective, and large variations in the crowd size has not been studied in-depth in the open literature. To address this gap, a systematic study on the robustness of four recently developed crowd--counting methods is performed in this paper to evaluate their performance with respect to variable (real-life) imaging scenarios that include different event types, weather conditions, image sources and crowd sizes. It is shown that the performance of the tested techniques is degraded in unclear weather conditions (i.e., fog, rain, snow) and also on images taken from large distances by drones. On the opposite, clear weather conditions, crowd--counting methods can provide accurate and usable results. |
Batagelj, Borut; Peer, Peter; Štruc, Vitomir; Dobrišek, Simon How to correctly detect face-masks for COVID-19 from visual information? Journal Article In: Applied sciences, vol. 11, no. 5, pp. 1-24, 2021, ISBN: 2076-3417. @article{Batagelj2021,
title = {How to correctly detect face-masks for COVID-19 from visual information?},
author = {Borut Batagelj and Peter Peer and Vitomir Štruc and Simon Dobrišek},
url = {https://www.mdpi.com/2076-3417/11/5/2070/pdf},
doi = {10.3390/app11052070},
isbn = {2076-3417},
year = {2021},
date = {2021-03-01},
journal = {Applied sciences},
volume = {11},
number = {5},
pages = {1-24},
abstract = {The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and (iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.},
keywords = {computer vision, COVID-19, deep learning, detection, face, mask detection, recognition},
pubstate = {published},
tppubtype = {article}
}
The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and (iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community. |
Oblak, Tim; Šircelj, Jaka; Struc, Vitomir; Peer, Peter; Solina, Franc; Jaklic, Aleš Learning to predict superquadric parameters from depth images with explicit and implicit supervision Journal Article In: IEEE Access, pp. 1-16, 2021, ISSN: 2169-3536. @article{Oblak2021,
title = {Learning to predict superquadric parameters from depth images with explicit and implicit supervision},
author = {Tim Oblak and Jaka Šircelj and Vitomir Struc and Peter Peer and Franc Solina and Aleš Jaklic
},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9274424},
doi = {10.1109/ACCESS.2020.3041584},
issn = {2169-3536},
year = {2021},
date = {2021-01-01},
journal = {IEEE Access},
pages = {1-16},
abstract = {Reconstruction of 3D space from visual data has always been a significant challenge in
the field of computer vision. A popular approach to address this problem can be found in the form of
bottom-up reconstruction techniques which try to model complex 3D scenes through a constellation of
volumetric primitives. Such techniques are inspired by the current understanding of the human visual
system and are, therefore, strongly related to the way humans process visual information, as suggested
by recent visual neuroscience literature. While advances have been made in recent years in the area of
3D reconstruction, the problem remains challenging due to the many possible ways of representing 3D
data, the ambiguity of determining the shape and general position in 3D space and the difficulty to train
efficient models for the prediction of volumetric primitives. In this paper, we address these challenges and
present a novel solution for recovering volumetric primitives from depth images. Specifically, we focus on
the recovery of superquadrics, a special type of parametric models able to describe a wide array of 3D
shapes using only a few parameters. We present a new learning objective that relies on the superquadric
(inside-outside) function and develop two learning strategies for training convolutional neural networks
(CNN) capable of predicting superquadric parameters. The first uses explicit supervision and penalizes the
difference between the predicted and reference superquadric parameters. The second strategy uses implicit
supervision and penalizes differences between the input depth images and depth images rendered from
the predicted parameters. CNN predictors for superquadric parameters are trained with both strategies and
evaluated on a large dataset of synthetic and real-world depth images. Experimental results show that both
strategies compare favourably to the existing state-of-the-art and result in high quality 3D reconstructions
of the modelled scenes at a much shorter processing time.},
keywords = {3d, computer vision, depth images, differential renderer, recovery, superquadric},
pubstate = {published},
tppubtype = {article}
}
Reconstruction of 3D space from visual data has always been a significant challenge in
the field of computer vision. A popular approach to address this problem can be found in the form of
bottom-up reconstruction techniques which try to model complex 3D scenes through a constellation of
volumetric primitives. Such techniques are inspired by the current understanding of the human visual
system and are, therefore, strongly related to the way humans process visual information, as suggested
by recent visual neuroscience literature. While advances have been made in recent years in the area of
3D reconstruction, the problem remains challenging due to the many possible ways of representing 3D
data, the ambiguity of determining the shape and general position in 3D space and the difficulty to train
efficient models for the prediction of volumetric primitives. In this paper, we address these challenges and
present a novel solution for recovering volumetric primitives from depth images. Specifically, we focus on
the recovery of superquadrics, a special type of parametric models able to describe a wide array of 3D
shapes using only a few parameters. We present a new learning objective that relies on the superquadric
(inside-outside) function and develop two learning strategies for training convolutional neural networks
(CNN) capable of predicting superquadric parameters. The first uses explicit supervision and penalizes the
difference between the predicted and reference superquadric parameters. The second strategy uses implicit
supervision and penalizes differences between the input depth images and depth images rendered from
the predicted parameters. CNN predictors for superquadric parameters are trained with both strategies and
evaluated on a large dataset of synthetic and real-world depth images. Experimental results show that both
strategies compare favourably to the existing state-of-the-art and result in high quality 3D reconstructions
of the modelled scenes at a much shorter processing time. |
Pernus, Martin; Struc, Vitomir; Dobrisek, Simon High Resolution Face Editing with Masked GAN Latent Code Optimization Journal Article In: CoRR, vol. abs/2103.11135, 2021. @article{DBLP:journals/corr/abs-2103-11135,
title = {High Resolution Face Editing with Masked GAN Latent Code Optimization},
author = {Martin Pernus and Vitomir Struc and Simon Dobrisek},
url = {https://arxiv.org/abs/2103.11135},
year = {2021},
date = {2021-01-01},
journal = {CoRR},
volume = {abs/2103.11135},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2020
|
Bortolato, Blaž; Ivanovska, Marija; Rot, Peter; Križaj, Janez; Terhorst, Philipp; Damer, Naser; Peer, Peter; Štruc, Vitomir Learning privacy-enhancing face representations through feature disentanglement Inproceedings In: Proceedings of FG 2020, IEEE, 2020. @inproceedings{BortolatoFG2020,
title = {Learning privacy-enhancing face representations through feature disentanglement},
author = {Blaž Bortolato and Marija Ivanovska and Peter Rot and Janez Križaj and Philipp Terhorst and Naser Damer and Peter Peer and Vitomir Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/07/FG2020___Learning_privacy_enhancing_face_representations_through_feature_disentanglement-1.pdf
},
year = {2020},
date = {2020-11-04},
booktitle = {Proceedings of FG 2020},
publisher = {IEEE},
abstract = {Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.},
keywords = {autoencoder, biometrics, CNN, disentaglement, face recognition, PFRNet, privacy, representation learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art. |
Vitek, M.; Das, A.; Pourcenoux, Y.; Missler, A.; Paumier, C.; Das, S.; Ghosh, I. De; Lucio, D. R.; Jr., L. A. Zanlorensi; Menotti, D.; Boutros, F.; Damer, N.; Grebe, J. H.; Kuijper, A.; Hu, J.; He, Y.; Wang, C.; Liu, H.; Wang, Y.; Sun, Z.; Osorio-Roig, D.; Rathgeb, C.; Busch, C.; Tapia, J.; Valenzuela, A.; Zampoukis, G.; Tsochatzidis, L.; Pratikakis, I.; Nathan, S.; Suganya, R.; Mehta, V.; Dhall, A.; Raja, K.; Gupta, G.; Khiarak, J. N.; Akbari-Shahper, M.; Jaryani, F.; Asgari-Chenaghlu, M.; Vyas, R.; Dakshit, S.; Dakshit, S.; Peer, P.; Pal, U.; Štruc, V. SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment Inproceedings In: International Joint Conference on Biometrics (IJCB 2020), pp. 1–10, 2020. @inproceedings{SSBC2020,
title = {SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment},
author = {M. Vitek and A. Das and Y. Pourcenoux and A. Missler and C. Paumier and S. Das and I. De Ghosh and D. R. Lucio and L. A. Zanlorensi Jr. and D. Menotti and F. Boutros and N. Damer and J. H. Grebe and A. Kuijper and J. Hu and Y. He and C. Wang and H. Liu and Y. Wang and Z. Sun and D. Osorio-Roig and C. Rathgeb and C. Busch and J. Tapia and A.~Valenzuela and G. Zampoukis and L. Tsochatzidis and I. Pratikakis and S. Nathan and R. Suganya and V. Mehta and A. Dhall and K. Raja and G. Gupta and J. N. Khiarak and M. Akbari-Shahper and F. Jaryani and M. Asgari-Chenaghlu and R. Vyas and S. Dakshit and S. Dakshit and P. Peer and U. Pal and V. Štruc},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/11/IJCB_SSBC_2020.pdf},
year = {2020},
date = {2020-09-28},
booktitle = {International Joint Conference on Biometrics (IJCB 2020)},
pages = {1--10},
abstract = {The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. },
keywords = {biometrics, competition IJCB, ocular, sclera, segmentation, SSBC},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. |
Marco Huber Philipp Terhörst, Naser Damer Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies Inproceedings In: Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG) 2020, pp. 1-5, IEEE, 2020, ISSN: 1617-5468. @inproceedings{Biosig_naser_2020,
title = {Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies},
author = {Philipp Terhörst, Marco Huber, Naser Damer, Peter Rot, Florian Kirchbuchner, Vitomir Struc, Arjan Kuijper},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/11/Biosig_privacy.pdf},
issn = {1617-5468},
year = {2020},
date = {2020-09-16},
booktitle = {Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG) 2020},
pages = {1-5},
publisher = {IEEE},
abstract = {Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.},
keywords = {face recognition, privacy, privacy protection, soft biometric privacy},
pubstate = {published},
tppubtype = {inproceedings}
}
Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography. |
Puc, Andraž; Štruc, Vitomir; Grm, Klemen Analysis of Race and Gender Bias in Deep Age Estimation Model Inproceedings In: Proceedings of EUSIPCO 2020, 2020. @inproceedings{GrmEUSIPCO2020,
title = {Analysis of Race and Gender Bias in Deep Age Estimation Model},
author = {Andraž Puc and Vitomir Štruc and Klemen Grm},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/07/race_and_gender_bias_eusipco-2.pdf},
year = {2020},
date = {2020-09-01},
booktitle = {Proceedings of EUSIPCO 2020},
abstract = {Due to advances in deep learning and convolutional neural networks (CNNs) there has been significant progress in the field of visual age estimation from face images over recent years. While today's models are able to achieve considerable age estimation accuracy, their behaviour, especially with respect to specific demographic groups is still not well understood. In this paper, we take a deeper look at CNN-based age estimation models and analyze their performance across different race and gender groups. We use two publicly available off-the-shelf age estimation models, i.e., FaceNet and WideResNet, for our study and analyze their performance on the UTKFace and APPA-REAL datasets. We partition face images into sub-groups based on race, gender and combinations of race and gender. We then compare age estimation results and find that there are noticeable differences in performance across demographics. Specifically, our results show that age estimation accuracy is consistently higher for men than for women, while race does not appear to have consistent effects on the tested models across different test datasets.
},
keywords = {age estimation, bias, bias analysis, biometrics, face analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
Due to advances in deep learning and convolutional neural networks (CNNs) there has been significant progress in the field of visual age estimation from face images over recent years. While today's models are able to achieve considerable age estimation accuracy, their behaviour, especially with respect to specific demographic groups is still not well understood. In this paper, we take a deeper look at CNN-based age estimation models and analyze their performance across different race and gender groups. We use two publicly available off-the-shelf age estimation models, i.e., FaceNet and WideResNet, for our study and analyze their performance on the UTKFace and APPA-REAL datasets. We partition face images into sub-groups based on race, gender and combinations of race and gender. We then compare age estimation results and find that there are noticeable differences in performance across demographics. Specifically, our results show that age estimation accuracy is consistently higher for men than for women, while race does not appear to have consistent effects on the tested models across different test datasets.
|
Terhorst, Philipp; Riehl, Kevin; Damer, Naser; Rot, Peter; Bortolato, Blaz; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, Arjan PE-MIU: a training-free privacy-enhancing face recognition approach based on minimum information units Journal Article In: IEEE Access, vol. 2020, 2020. @article{PEMIU_Access2020,
title = {PE-MIU: a training-free privacy-enhancing face recognition approach based on minimum information units},
author = {Philipp Terhorst and Kevin Riehl and Naser Damer and Peter Rot and Blaz Bortolato and Florian Kirchbuchner and Vitomir Struc and Arjan Kuijper},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9094207},
year = {2020},
date = {2020-06-02},
journal = {IEEE Access},
volume = {2020},
abstract = {Research on soft-biometrics showed that privacy-sensitive information can be deduced from
biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity,
sexual orientation, and health state can be deduced. For many applications, these templates are expected
to be used for recognition purposes only. Thus, extracting this information raises major privacy issues.
Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide
strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition
approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a
privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements
and is not limited to pre-defined attributes. We exploit the structural differences between face recognition
and facial attribute estimation by creating templates in a mixed representation of minimal information
units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form.
Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification,
these units of a probe template are assigned to the units of a reference template by solving an optimal
best-matching problem. This allows our approach to maintain a high recognition ability. The experiments
are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover,
we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism.
The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly
higher degree than previous work in all investigated scenarios. At the same time, our solution is able to
achieve a verification performance close to that of the unmodified recognition system. Unlike previous
works, our approach offers a strong and comprehensive privacy-enhancement without the need of training},
keywords = {biometrics, face recognition, minimal information units, privacy, soft biometric privacy, soft biometrics},
pubstate = {published},
tppubtype = {article}
}
Research on soft-biometrics showed that privacy-sensitive information can be deduced from
biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity,
sexual orientation, and health state can be deduced. For many applications, these templates are expected
to be used for recognition purposes only. Thus, extracting this information raises major privacy issues.
Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide
strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition
approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a
privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements
and is not limited to pre-defined attributes. We exploit the structural differences between face recognition
and facial attribute estimation by creating templates in a mixed representation of minimal information
units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form.
Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification,
these units of a probe template are assigned to the units of a reference template by solving an optimal
best-matching problem. This allows our approach to maintain a high recognition ability. The experiments
are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover,
we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism.
The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly
higher degree than previous work in all investigated scenarios. At the same time, our solution is able to
achieve a verification performance close to that of the unmodified recognition system. Unlike previous
works, our approach offers a strong and comprehensive privacy-enhancement without the need of training |
Šircelj, Jaka; Oblak, Tim; Grm, Klemen; Petković, Uroš; Jaklič, Aleš; Peer, Peter; Štruc, Vitomir; Solina, Franc Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks Inproceedings In: 25th Computer Vision Winter Workshop (CVWW 2020), 2020. @inproceedings{sircelj2020sqcnn,
title = {Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks},
author = {Jaka Šircelj and Tim Oblak and Klemen Grm and Uroš Petković and Aleš Jaklič and Peter Peer and Vitomir Štruc and Franc Solina},
url = {https://lmi.fe.uni-lj.si/en/sircelj2020cvww/
https://arxiv.org/abs/2001.10504},
year = {2020},
date = {2020-02-03},
booktitle = {25th Computer Vision Winter Workshop (CVWW 2020)},
abstract = {In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around convolutional neural networks (CNNs) capable of segmenting complex depth scenes into the simpler geometric structures that can be represented with superquadric models. In the first stage, our approach uses a Mask RCNN model to identify superquadric-like structures in depth scenes and then fits superquadric models to the segmented structures using a specially designed CNN regressor. Using our approach we are able to describe complex structures with a small number of interpretable parameters. We evaluated the proposed approach on synthetic as well as real-world depth data and show that our solution does not only result in competitive performance in comparison to the state-of-the-art, but is able to decompose scenes into a number of superquadric models at a fraction of the time required by competing approaches. We make all data and models used in the paper available from https://lmi.fe.uni-lj.si/en/research/resources/sq-seg.},
keywords = {CNN, convolutional neural networks, segmentation, superquadrics, volumetric data},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around convolutional neural networks (CNNs) capable of segmenting complex depth scenes into the simpler geometric structures that can be represented with superquadric models. In the first stage, our approach uses a Mask RCNN model to identify superquadric-like structures in depth scenes and then fits superquadric models to the segmented structures using a specially designed CNN regressor. Using our approach we are able to describe complex structures with a small number of interpretable parameters. We evaluated the proposed approach on synthetic as well as real-world depth data and show that our solution does not only result in competitive performance in comparison to the state-of-the-art, but is able to decompose scenes into a number of superquadric models at a fraction of the time required by competing approaches. We make all data and models used in the paper available from https://lmi.fe.uni-lj.si/en/research/resources/sq-seg. |
Stepec, Dejan; Emersic, Ziga; Peer, Peter; Struc, Vitomir Constellation-Based Deep Ear Recognition Incollection In: Jiang, R.; Li, CT.; Crookes, D.; Meng, W.; Rosenberger, C. (Ed.): Deep Biometrics: Unsupervised and Semi-Supervised Learning, Springer, 2020, ISBN: 978-3-030-32582-4. @incollection{Stepec2020COMEar,
title = {Constellation-Based Deep Ear Recognition},
author = {Dejan Stepec and Ziga Emersic and Peter Peer and Vitomir Struc},
editor = {R. Jiang and CT. Li and D. Crookes and W. Meng and C. Rosenberger},
url = {https://link.springer.com/chapter/10.1007/978-3-030-32583-1_8
https://lmi.fe.uni-lj.si/wp-content/uploads/2020/02/DeepBio2019___REMIX.pdf},
doi = {https://doi.org/10.1007/978-3-030-32583-1_8},
isbn = {978-3-030-32582-4},
year = {2020},
date = {2020-01-29},
booktitle = {Deep Biometrics: Unsupervised and Semi-Supervised Learning},
publisher = {Springer},
abstract = {This chapter introduces COM-Ear, a deep constellation model for ear recognition. Different from competing solutions, COM-Ear encodes global as well as local characteristics of ear images and generates descriptive ear representations that ensure competitive recognition performance. The model is designed as dual-path convolutional neural network (CNN), where one path processes the input in a holistic manner, and the second captures local images characteristics from image patches sampled from the input image. A novel pooling operation, called patch-relevant-information pooling, is also proposed and integrated into the COM-Ear model. The pooling operation helps to select features from the input patches that are locally important and to focus the attention of the network to image regions that are descriptive and important for representation purposes. The model is trained in an end-to-end manner using a combined cross-entropy and center loss. Extensive experiments on the recently introduced Extended Annotated Web Ears (AWEx).},
keywords = {biometrics, CNN, deep learning, ear recognition, neural networks},
pubstate = {published},
tppubtype = {incollection}
}
This chapter introduces COM-Ear, a deep constellation model for ear recognition. Different from competing solutions, COM-Ear encodes global as well as local characteristics of ear images and generates descriptive ear representations that ensure competitive recognition performance. The model is designed as dual-path convolutional neural network (CNN), where one path processes the input in a holistic manner, and the second captures local images characteristics from image patches sampled from the input image. A novel pooling operation, called patch-relevant-information pooling, is also proposed and integrated into the COM-Ear model. The pooling operation helps to select features from the input patches that are locally important and to focus the attention of the network to image regions that are descriptive and important for representation purposes. The model is trained in an end-to-end manner using a combined cross-entropy and center loss. Extensive experiments on the recently introduced Extended Annotated Web Ears (AWEx). |
Grm, Klemen; Scheirer, Walter J.; Štruc, Vitomir Face hallucination using cascaded super-resolution and identity priors Journal Article In: IEEE Transactions on Image Processing, 2020. @article{TIPKlemen_2020,
title = {Face hallucination using cascaded super-resolution and identity priors},
author = {Klemen Grm and Walter J. Scheirer and Vitomir Štruc},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8866753
https://lmi.fe.uni-lj.si/wp-content/uploads/2023/02/IEEET_face_hallucination_compressed.pdf},
doi = {10.1109/TIP.2019.2945835},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {IEEE Transactions on Image Processing},
abstract = {In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the lowresolution facial images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from most competing super-resolution techniques that rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of 2×. This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. The proposed C-SRIP model (Cascaded Super Resolution with Identity Priors) is able to upscale (tiny) low-resolution images captured in unconstrained conditions and produce visually convincing results for diverse low-resolution inputs. We rigorously evaluate the proposed model on the Labeled Faces in the Wild (LFW), Helen and CelebA datasets and report superior performance compared to the existing state-of-the-art.
},
keywords = {biometrics, CNN, computer vision, deep learning, face, face hallucination, super-resolution},
pubstate = {published},
tppubtype = {article}
}
In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the lowresolution facial images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from most competing super-resolution techniques that rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of 2×. This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. The proposed C-SRIP model (Cascaded Super Resolution with Identity Priors) is able to upscale (tiny) low-resolution images captured in unconstrained conditions and produce visually convincing results for diverse low-resolution inputs. We rigorously evaluate the proposed model on the Labeled Faces in the Wild (LFW), Helen and CelebA datasets and report superior performance compared to the existing state-of-the-art.
|
Vitek, Matej; Rot, Peter; Struc, Vitomir; Peer, Peter A comprehensive investigation into sclera biometrics: a novel dataset and performance study Journal Article In: Neural Computing and Applications, pp. 1-15, 2020. @article{vitek2020comprehensive,
title = {A comprehensive investigation into sclera biometrics: a novel dataset and performance study},
author = {Matej Vitek and Peter Rot and Vitomir Struc and Peter Peer},
url = {https://link.springer.com/epdf/10.1007/s00521-020-04782-1},
doi = {https://doi.org/10.1007/s00521-020-04782-1},
year = {2020},
date = {2020-01-01},
journal = {Neural Computing and Applications},
pages = {1-15},
abstract = {The area of ocular biometrics is among the most popular branches of biometric recognition technology. This area has long been dominated by iris recognition research, while other ocular modalities such as the periocular region or the vasculature of the sclera have received significantly less attention in the literature. Consequently, ocular modalities beyond the iris are not well studied and their characteristics are today still not as well understood. While recent needs for more secure authentication schemes have considerably increased the interest in competing ocular modalities, progress in these areas is still held back by the lack of publicly available datasets that would allow for more targeted research into specific ocular characteristics next to the iris. In this paper, we aim to bridge this gap for the case of sclera biometrics and introduce a novel dataset designed for research into ocular biometrics and most importantly for research into the vasculature of the sclera. Our dataset, called Sclera Blood Vessels, Periocular and Iris (SBVPI), is, to the best of our knowledge, the first publicly available dataset designed specifically with research in sclera biometrics in mind. The dataset contains high-quality RGB ocular images, captured in the visible spectrum, belonging to 55 subjects. Unlike competing datasets, it comes with manual markups of various eye regions, such as the iris, pupil, canthus or eyelashes and a detailed pixel-wise annotation of the complete sclera vasculature for a subset of the images. Additionally, the datasets ship with gender and age labels. The unique characteristics of the dataset allow us to study aspects of sclera biometrics technology that have not been studied before in the literature (e.g. vasculature segmentation techniques) as well as issues that are of key importance for practical recognition systems. Thus, next to the SBVPI dataset we also present in this paper a comprehensive investigation into sclera biometrics and the main covariates that affect the performance of sclera segmentation and recognition techniques, such as gender, age, gaze direction or image resolution. Our experiments not only demonstrate the usefulness of the newly introduced dataset, but also contribute to a better understanding of sclera biometrics in general.},
keywords = {biometrics, CNN, dataset, multi-view, ocular, performance study, recognition, sclera, segmentation, visible light},
pubstate = {published},
tppubtype = {article}
}
The area of ocular biometrics is among the most popular branches of biometric recognition technology. This area has long been dominated by iris recognition research, while other ocular modalities such as the periocular region or the vasculature of the sclera have received significantly less attention in the literature. Consequently, ocular modalities beyond the iris are not well studied and their characteristics are today still not as well understood. While recent needs for more secure authentication schemes have considerably increased the interest in competing ocular modalities, progress in these areas is still held back by the lack of publicly available datasets that would allow for more targeted research into specific ocular characteristics next to the iris. In this paper, we aim to bridge this gap for the case of sclera biometrics and introduce a novel dataset designed for research into ocular biometrics and most importantly for research into the vasculature of the sclera. Our dataset, called Sclera Blood Vessels, Periocular and Iris (SBVPI), is, to the best of our knowledge, the first publicly available dataset designed specifically with research in sclera biometrics in mind. The dataset contains high-quality RGB ocular images, captured in the visible spectrum, belonging to 55 subjects. Unlike competing datasets, it comes with manual markups of various eye regions, such as the iris, pupil, canthus or eyelashes and a detailed pixel-wise annotation of the complete sclera vasculature for a subset of the images. Additionally, the datasets ship with gender and age labels. The unique characteristics of the dataset allow us to study aspects of sclera biometrics technology that have not been studied before in the literature (e.g. vasculature segmentation techniques) as well as issues that are of key importance for practical recognition systems. Thus, next to the SBVPI dataset we also present in this paper a comprehensive investigation into sclera biometrics and the main covariates that affect the performance of sclera segmentation and recognition techniques, such as gender, age, gaze direction or image resolution. Our experiments not only demonstrate the usefulness of the newly introduced dataset, but also contribute to a better understanding of sclera biometrics in general. |
2019
|
Rot, Peter; Vitek, Matej; Grm, Klemen; Emeršič, Žiga; Peer, Peter; Štruc, Vitomir Deep Sclera Segmentation and Recognition Incollection In: Uhl, Andreas; Busch, Christoph; Marcel, Sebastien; Veldhuis, Rainer (Ed.): Handbook of Vascular Biometrics, pp. 395-432, Springer, 2019, ISBN: 978-3-030-27731-4. @incollection{ScleraNetChapter,
title = {Deep Sclera Segmentation and Recognition},
author = {Peter Rot and Matej Vitek and Klemen Grm and Žiga Emeršič and Peter Peer
and Vitomir Štruc},
editor = {Andreas Uhl and Christoph Busch and Sebastien Marcel and Rainer Veldhuis},
url = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-27731-4_13.pdf},
doi = {https://doi.org/10.1007/978-3-030-27731-4_13},
isbn = {978-3-030-27731-4},
year = {2019},
date = {2019-11-14},
booktitle = {Handbook of Vascular Biometrics},
pages = {395-432},
publisher = {Springer},
chapter = {13},
series = {Advances in Computer Vision and Pattern Recognition},
abstract = {In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich set of annotations, such as a per-pixel markup of various eye parts (including the sclera vasculature), identity, gaze-direction and gender labels. We conduct rigorous experiments on SBVPI with competing techniques from the literature and show that the combination of the proposed segmentation and descriptor-computation models results in highly competitive recognition performance.},
keywords = {biometrics, CNN, deep learning, ocular, sclera, segmentation, vasculature},
pubstate = {published},
tppubtype = {incollection}
}
In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich set of annotations, such as a per-pixel markup of various eye parts (including the sclera vasculature), identity, gaze-direction and gender labels. We conduct rigorous experiments on SBVPI with competing techniques from the literature and show that the combination of the proposed segmentation and descriptor-computation models results in highly competitive recognition performance. |
Krizaj, Janez; Peer, Peter; Struc, Vitomir; Dobrisek, Simon Simultaneous multi-decent regression and feature learning for landmarking in depth image Journal Article In: Neural Computing and Applications, 2019, ISBN: 0941-0643. @article{Krizaj3Docalization,
title = {Simultaneous multi-decent regression and feature learning for landmarking in depth image},
author = {Janez Krizaj and Peter Peer and Vitomir Struc and Simon Dobrisek},
url = {https://link.springer.com/content/pdf/10.1007%2Fs00521-019-04529-7.pdf},
doi = {https://doi.org/10.1007/s00521-019-04529-7},
isbn = {0941-0643},
year = {2019},
date = {2019-10-01},
journal = {Neural Computing and Applications},
abstract = {Face alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations.},
keywords = {3d, biometrics, depth data, face alignment, face analysis, landmarking},
pubstate = {published},
tppubtype = {article}
}
Face alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations. |
Oblak, Tim; Grm, Klemen; Jaklič, Aleš; Peer, Peter; Štruc, Vitomir; Solina, Franc Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study Inproceedings In: 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 45-52, IEEE, 2019. @inproceedings{oblak2019recovery,
title = {Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study},
author = {Tim Oblak and Klemen Grm and Aleš Jaklič and Peter Peer and Vitomir Štruc and Franc Solina},
url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Superkvadriki_draft.pdf},
year = {2019},
date = {2019-06-01},
booktitle = {2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},
journal = {arXiv preprint arXiv:1904.06585},
pages = {45-52},
publisher = {IEEE},
abstract = {It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings. Such models are typically motivated by human visual perception and aim to represents all elements of the physical word ranging from individual objects to complex scenes using a small set of parameters. One of the de facto standards to approach this problem are superquadrics - volumetric models that define various 3D shape primitives and can be fitted to actual 3D data (either in the form of point clouds or range images). However, existing solutions to superquadric recovery involve costly iterative fitting procedures, which limit the applicability of such techniques in practice. To alleviate this problem, we explore in this paper the possibility to recover superquadrics from range images without time consuming iterative parameter estimation techniques by using contemporary deep-learning models, more specifically, convolutional neural networks (CNNs). We pose the superquadric recovery problem as a regression task and develop a CNN regressor that is able to estimate the parameters of a superquadric model from a given range image. We train the regressor on a large set of synthetic range images, each containing a single (unrotated) superquadric shape and evaluate the learned model in comparative experiments with the current state-of-the-art. Additionally, we also present a qualitative analysis involving a dataset of real-world objects. The results of our experiments show that the proposed regressor not only outperforms the existing state-of-the-art, but also ensures a 270x faster execution time.},
keywords = {CNN, convolutional neural networks, superquadrics, volumetric data},
pubstate = {published},
tppubtype = {inproceedings}
}
It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings. Such models are typically motivated by human visual perception and aim to represents all elements of the physical word ranging from individual objects to complex scenes using a small set of parameters. One of the de facto standards to approach this problem are superquadrics - volumetric models that define various 3D shape primitives and can be fitted to actual 3D data (either in the form of point clouds or range images). However, existing solutions to superquadric recovery involve costly iterative fitting procedures, which limit the applicability of such techniques in practice. To alleviate this problem, we explore in this paper the possibility to recover superquadrics from range images without time consuming iterative parameter estimation techniques by using contemporary deep-learning models, more specifically, convolutional neural networks (CNNs). We pose the superquadric recovery problem as a regression task and develop a CNN regressor that is able to estimate the parameters of a superquadric model from a given range image. We train the regressor on a large set of synthetic range images, each containing a single (unrotated) superquadric shape and evaluate the learned model in comparative experiments with the current state-of-the-art. Additionally, we also present a qualitative analysis involving a dataset of real-world objects. The results of our experiments show that the proposed regressor not only outperforms the existing state-of-the-art, but also ensures a 270x faster execution time. |
Emeršič, Žiga; V., A. Kumar S.; Harish, B. S.; Gutfeter, W.; Khiarak, J. N.; Pacut, A.; Hansley, E.; Segundo, M. Pamplona; Sarkar, S.; Park, H.; Nam, G. Pyo; Kim, I. J.; Sangodkar, S. G.; Kacar, U.; Kirci, M.; Yuan, L.; Yuan, J.; Zhao, H.; Lu, F.; Mao, J.; Zhang, X.; Yaman, D.; Eyiokur, F. I.; Ozler, K. B.; Ekenel, H. K.; Chowdhury, D. Paul; Bakshi, S.; Sa, P. K.; Majhni, B.; Peer, P.; Štruc, V. The Unconstrained Ear Recognition Challenge 2019 Inproceedings In: International Conference on Biometrics (ICB 2019), 2019. @inproceedings{emervsivc2019unconstrained,
title = {The Unconstrained Ear Recognition Challenge 2019},
author = {Žiga Emeršič and A. Kumar S. V. and B. S. Harish and W. Gutfeter and J. N. Khiarak and A. Pacut and E. Hansley and M. Pamplona Segundo and S. Sarkar and H. Park and G. Pyo Nam and I. J. Kim and S.G. Sangodkar and U. Kacar and M. Kirci and L. Yuan and J. Yuan and H. Zhao and F. Lu and J. Mao and X. Zhang and D. Yaman and F. I. Eyiokur and K. B. Ozler and H. K. Ekenel and D. Paul Chowdhury and S. Bakshi and P. K. Sa and B. Majhni and P. Peer and V. Štruc},
url = {https://arxiv.org/pdf/1903.04143.pdf},
year = {2019},
date = {2019-06-01},
booktitle = {International Conference on Biometrics (ICB 2019)},
journal = {arXiv preprint arXiv:1903.04143},
abstract = {This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i.e. gender and ethnicity. Research groups from 12 institutions entered the competition and submitted a total of 13 recognition approaches ranging from descriptor-based methods to deep-learning models. The majority of submissions focused on ensemble based methods combining either representations from multiple deep models or hand-crafted with learned image descriptors. Our analysis shows that methods incorporating deep learning models clearly outperform techniques relying solely on hand-crafted descriptors, even though both groups of techniques exhibit similar behaviour when it comes to robustness to various covariates, such presence of occlusions, changes in (head) pose, or variability in image resolution. The results of the challenge also show that there has been considerable progress since the first UERC in 2017, but that there is still ample room for further research in this area.},
keywords = {biometrics, ear, ear recognitoin, uerc 2019},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i.e. gender and ethnicity. Research groups from 12 institutions entered the competition and submitted a total of 13 recognition approaches ranging from descriptor-based methods to deep-learning models. The majority of submissions focused on ensemble based methods combining either representations from multiple deep models or hand-crafted with learned image descriptors. Our analysis shows that methods incorporating deep learning models clearly outperform techniques relying solely on hand-crafted descriptors, even though both groups of techniques exhibit similar behaviour when it comes to robustness to various covariates, such presence of occlusions, changes in (head) pose, or variability in image resolution. The results of the challenge also show that there has been considerable progress since the first UERC in 2017, but that there is still ample room for further research in this area. |
Grm, Klemen; Pernus, Martin; Cluzel, Leo; Scheirer, Walter J.; Dobrisek, Simon; Struc, Vitomir Face Hallucination Revisited: An Exploratory Study on Dataset Bias Inproceedings In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019. @inproceedings{grm2019face,
title = {Face Hallucination Revisited: An Exploratory Study on Dataset Bias},
author = {Klemen Grm and Martin Pernus and Leo Cluzel and Walter J. Scheirer and Simon Dobrisek and Vitomir Struc},
url = {http://openaccess.thecvf.com/content_CVPRW_2019/papers/Biometrics/Grm_Face_Hallucination_Revisited_An_Exploratory_Study_on_Dataset_Bias_CVPRW_2019_paper.pdf
https://arxiv.org/pdf/1812.09010.pdf},
year = {2019},
date = {2019-06-01},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops},
abstract = {Contemporary face hallucination (FH) models exhibit considerable ability to reconstruct high-resolution (HR) details from low-resolution (LR) face images. This ability is commonly learned from examples of corresponding HR-LR image pairs, created by artificially down-sampling the HR ground truth data. This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle. If the image characteristics encountered with real-world LR images differ from the ones seen during training, FH models are still expected to perform well, but in practice may not produce the desired results. In this paper we study this problem and explore the bias introduced into FH models by the characteristics of the training data. We systematically analyze the generalization capabilities of several FH models in various scenarios where the degradation function does not match the training setup and conduct experiments with synthetically downgraded as well as real-life low-quality images. We make several interesting findings that provide insight into existing problems with FH models and point to future research directions.},
keywords = {dataset bias, face, face hallucination, super-resolution},
pubstate = {published},
tppubtype = {inproceedings}
}
Contemporary face hallucination (FH) models exhibit considerable ability to reconstruct high-resolution (HR) details from low-resolution (LR) face images. This ability is commonly learned from examples of corresponding HR-LR image pairs, created by artificially down-sampling the HR ground truth data. This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle. If the image characteristics encountered with real-world LR images differ from the ones seen during training, FH models are still expected to perform well, but in practice may not produce the desired results. In this paper we study this problem and explore the bias introduced into FH models by the characteristics of the training data. We systematically analyze the generalization capabilities of several FH models in various scenarios where the degradation function does not match the training setup and conduct experiments with synthetically downgraded as well as real-life low-quality images. We make several interesting findings that provide insight into existing problems with FH models and point to future research directions. |