2022 |
Tomašecić, Darian; Peer, Peter; Solina, Franc; Jaklič, Aleš; Štruc, Vitomir Reconstructing Superquadrics from Intensity and Color Images Članek v strokovni reviji V: Sensors, vol. 22, iss. 4, no. 5332, 2022. Povzetek | Povezava | BibTeX | Oznake: arrs, CNN, depth data, depth estimation, depth sensing, intensity images, superquadric, superquadrics @article{TomasevicSensors,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 Proceedings Article V: IAPR International Conference on Pattern Recognition (ICPR), 2022. Povzetek | Povezava | BibTeX | Oznake: adversarial examples, adversarial noise, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment @inproceedings{ICPR2022,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 Proceedings Article V: EUSIPCO 2022, 2022. Povzetek | Povezava | BibTeX | Oznake: bias, bias analysis, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment @inproceedings{EUSIPCO_2022,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 Članek v strokovni reviji V: IEEE Transactions on Biometrics, Identity and Behavior (TBIOM), vol. 4, iss. 2, str. 263-275, 2022. Povzetek | Povezava | BibTeX | Oznake: attack, face recognition, privacy, privacy enhancement, privacy protection, privacy-enhancing techniques, soft biometric privacy @article{TBIOM_2022,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 Proceedings Article V: International Workshop on Biometrics and Forensics (IWBF), str. 1–6, 2022. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, deep learning, ear, ear biometrics, kinear, kinship, kinship recognition, transformer @inproceedings{KinEars,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 Proceedings Article V: Proceedings of Rosus 2022, 2022. Povzetek | Povezava | BibTeX | Oznake: deepbeauty, računalniški vid, segmentacija @inproceedings{Rosus2022,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 Članek v strokovni reviji V: Sensors, iss. 6, no. 2388, str. 1-26, 2022. Povzetek | Povezava | BibTeX | Oznake: 3d face, biometrics, face, face analysis, face images, face recognition @article{KrizajSensors2022,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 Proceedings Article V: International Conference on Artificial Intelligence in Information and Communication (ICAIIC), IEEE, 2022, ISBN: 978-1-6654-5818-4. Povzetek | Povezava | BibTeX | Oznake: body segmentation, cn, CNN, computer vision, deep beauty, deep learning, multi-task learning, segmentation, virtual try-on @inproceedings{JulijanJugBody,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 Proceedings Article V: IEEE/CVF Winter Applications in Computer Vision (WACV), str. 1–10, 2022. Povzetek | Povezava | BibTeX | Oznake: computer vision, deepbeauty, fashion, generative models, image editing, try-on, virtual try-on @inproceedings{WACV2022_Fele,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 Članek v strokovni reviji V: Sensors, vol. 22, no. 1, str. 1-26, 2022. Povzetek | Povezava | BibTeX | Oznake: biometrics; computer vision; deep learning; palmprints @article{Stoimchev2022,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 Book Section V: Rathgeb, Christian; Tolosana, Ruben; Vera-Rodriguez, Ruben; Busch, Christoph (Ur.): Handbook of Digital Face Manipulation and Detection, 2022. Povezava | BibTeX | Oznake: biometrics, face, privacy, privacy enhancement, privacy-enhancing techniques, soft biometric privacy @incollection{RotManipulationBook, |
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 Book Section V: Rathgeb, Christian; Tolosana, Ruben; Vera-Rodriguez, Ruben; Busch, Christoph (Ur.): Handbook of Digital Face Manipulation and Detection, str. 463–482, 2022, ISBN: 978-3-030-87663-0. Povzetek | Povezava | BibTeX | Oznake: @incollection{ManipulationFace2022,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 Članek v strokovni reviji V: IEEE Access, str. 1–17, 2021, ISSN: 2169-3536. Povzetek | Povezava | BibTeX | Oznake: biometrics, contextual information, deep leraning, ear detection, ear recognition, ear segmentation, neural networks, segmentation @article{ContexedNet_Emersic_2021,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 Proceedings Article V: Proceedings of ERK 2021, str. 1–4, 2021. Povzetek | Povezava | BibTeX | Oznake: biometrics, comparative study, computer vision, deepfake detection, deepfakes, detection, face, one-class learning @inproceedings{ERK_Marija_2021,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 Proceedings Article V: Proceedings of ERK 2021, str. 1-4, 2021. Povzetek | Povezava | BibTeX | Oznake: CNN, deep learning, face, face hallucination, frequency encoding, super-resolution @inproceedings{Grm-SuperResolution_ERK2021,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 Proceedings Article V: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. Povzetek | Povezava | BibTeX | Oznake: biometrics, face recognition, masks @inproceedings{MFR_IJCB2021,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 Proceedings Article V: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. Povzetek | Povezava | BibTeX | Oznake: biometrics, competition, iris, segmentation @inproceedings{NIR_IJCB2021,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 Članek v strokovni reviji V: IEEE Transactions on Information Forensics and Security, vol. 16, str. 4147-4183, 2021. Povzetek | Povezava | BibTeX | Oznake: biometrics, deidentification, face analysis, face deidentification, face recognition, face verification, FaceGEN, privacy, privacy protection, privacy-enhancing techniques, soft biometric privacy @article{TIFS_PrivacySurveyb,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 Članek v strokovni reviji V: Elektorethniski Vestnik, vol. 88, iss. 5, str. 227-235, 2021. Povzetek | Povezava | BibTeX | Oznake: CNN, crowd counting, drones, image characteristics, model comparison, neural networks @article{CrowdCountingPevec,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? Članek v strokovni reviji V: Applied sciences, vol. 11, no. 5, str. 1-24, 2021, ISBN: 2076-3417. Povzetek | Povezava | BibTeX | Oznake: computer vision, COVID-19, deep learning, detection, face, mask detection, recognition @article{Batagelj2021,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 Članek v strokovni reviji V: IEEE Access, str. 1-16, 2021, ISSN: 2169-3536. Povzetek | Povezava | BibTeX | Oznake: 3d, computer vision, depth images, differential renderer, recovery, superquadric @article{Oblak2021,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 Članek v strokovni reviji V: CoRR, vol. abs/2103.11135, 2021. @article{DBLP:journals/corr/abs-2103-11135, |
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 Proceedings Article V: Proceedings of FG 2020, IEEE, 2020. Povzetek | Povezava | BibTeX | Oznake: autoencoder, biometrics, CNN, disentaglement, face recognition, PFRNet, privacy, representation learning @inproceedings{BortolatoFG2020,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 Proceedings Article V: International Joint Conference on Biometrics (IJCB 2020), str. 1–10, 2020. Povzetek | Povezava | BibTeX | Oznake: biometrics, competition IJCB, ocular, sclera, segmentation, SSBC @inproceedings{SSBC2020,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 Proceedings Article V: Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG) 2020, str. 1-5, IEEE, 2020, ISSN: 1617-5468. Povzetek | Povezava | BibTeX | Oznake: face recognition, privacy, privacy protection, soft biometric privacy @inproceedings{Biosig_naser_2020,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 Proceedings Article V: Proceedings of EUSIPCO 2020, 2020. Povzetek | Povezava | BibTeX | Oznake: age estimation, bias, bias analysis, biometrics, face analysis @inproceedings{GrmEUSIPCO2020,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 Članek v strokovni reviji V: IEEE Access, vol. 2020, 2020. Povzetek | Povezava | BibTeX | Oznake: biometrics, face recognition, minimal information units, privacy, soft biometric privacy, soft biometrics @article{PEMIU_Access2020,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 Proceedings Article V: 25th Computer Vision Winter Workshop (CVWW 2020), 2020. Povzetek | Povezava | BibTeX | Oznake: CNN, convolutional neural networks, segmentation, superquadrics, volumetric data @inproceedings{sircelj2020sqcnn,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 Book Section V: Jiang, R.; Li, CT.; Crookes, D.; Meng, W.; Rosenberger, C. (Ur.): Deep Biometrics: Unsupervised and Semi-Supervised Learning, Springer, 2020, ISBN: 978-3-030-32582-4. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, deep learning, ear recognition, neural networks @incollection{Stepec2020COMEar,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 Članek v strokovni reviji V: IEEE Transactions on Image Processing, 2020. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, computer vision, deep learning, face, face hallucination, super-resolution @article{TIPKlemen_2020,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 Članek v strokovni reviji V: Neural Computing and Applications, str. 1-15, 2020. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, dataset, multi-view, ocular, performance study, recognition, sclera, segmentation, visible light @article{vitek2020comprehensive,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 Book Section V: Uhl, Andreas; Busch, Christoph; Marcel, Sebastien; Veldhuis, Rainer (Ur.): Handbook of Vascular Biometrics, str. 395-432, Springer, 2019, ISBN: 978-3-030-27731-4. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, deep learning, ocular, sclera, segmentation, vasculature @incollection{ScleraNetChapter,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 Članek v strokovni reviji V: Neural Computing and Applications, 2019, ISBN: 0941-0643. Povzetek | Povezava | BibTeX | Oznake: 3d, biometrics, depth data, face alignment, face analysis, landmarking @article{Krizaj3Docalization,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 Proceedings Article V: 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 45-52, IEEE, 2019. Povzetek | Povezava | BibTeX | Oznake: CNN, convolutional neural networks, superquadrics, volumetric data @inproceedings{oblak2019recovery,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 Proceedings Article V: International Conference on Biometrics (ICB 2019), 2019. Povzetek | Povezava | BibTeX | Oznake: biometrics, ear, ear recognitoin, uerc 2019 @inproceedings{emervsivc2019unconstrained,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 Proceedings Article V: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019. Povzetek | Povezava | BibTeX | Oznake: dataset bias, face, face hallucination, super-resolution @inproceedings{grm2019face,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. |
Kovač, Jure; Štruc, Vitomir; Peer, Peter Frame-based classification for cross-speed gait recognition Članek v strokovni reviji V: Multimedia Tools and Applications, vol. 78, no. 5, str. 5621–5643, 2019, ISSN: 1573-7721. Povzetek | Povezava | BibTeX | Oznake: biometrics, gait recognition @article{kovavc2019frame,The use of human gait as the means of biometric identification has gained a lot of attention in the past few years, mostly due to its enormous potential. Such biometrics can be captured at public places from a distance without subjects collaboration, awareness and even consent. However, there are still numerous challenges caused by influence of covariate factors like changes of walking speed, view, clothing, footwear etc., that have negative impact on recognition performance. In this paper we tackle walking speed changes with a skeleton model-based gait recognition system focusing on improving algorithm robustness and improving the performance at higher walking speed changes. We achieve these by proposing frame based classification method, which overcomes the main shortcoming of distance based classification methods, which are very sensitive to gait cycle starting point detection. The proposed technique is starting point invariant with respect to gait cycle starts and as such ensures independence of classification from gait cycle start positions. Additionally, we propose wavelet transform based signal approximation, which enables the analysis of feature signals on different frequency space resolutions and diminishes the need for using feature transformation that require training. With the evaluation on OU-ISIR gait dataset we demonstrate state of the art performance of proposed methods. |
Lozej, Juš; Štepec, Dejan; Štruc, Vitomir; Peer, Peter Influence of segmentation on deep iris recognition performance Proceedings Article V: 7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF 2019), 2019. Povzetek | Povezava | BibTeX | Oznake: biometrics, iris, ocular, segmentation @inproceedings{lozej2019influence,Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies on methodologies akin to traditional iris recognition pipelines. Hence, the proposed models do not approach iris recognition in an end-to-end manner, but rather use standard heuristic iris segmentation (and unwrapping) techniques to produce normalized inputs for the deep learning models. However, because deep learning is able to model very complex data distributions and nonlinear data changes, an obvious question arises. How important is the use of traditional segmentation methods in a deep learning setting? To answer this question, we present in this paper an empirical analysis of the impact of iris segmentation on the performance of deep learning models using a simple two stage pipeline consisting of a segmentation and a recognition step. We evaluate how the accuracy of segmentation influences recognition performance but also examine if segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for the experiments and report several interesting findings. |
Ziga, Emersic; Janez, Krizaj; Vitomir, Struc; Peter, Peer Deep ear recognition pipeline Book Section V: Mahmoud, Hassaballah; M., Hosny Khalid (Ur.): Recent advances in computer vision : theories and applications, vol. 804, Springer, 2019, ISBN: 1860-9503. Povzetek | Povezava | BibTeX | Oznake: ear, ear recognition, pipeline @incollection{ZigaBook2019,Ear recognition has seen multiple improvements in recent years and still remains very active today. However, it has been approached from recognition and detection perspective separately. Furthermore, deep-learning-based approaches that are popular in other domains have seen limited use in ear recognition and even more so in ear detection. Moreover, to obtain a usable recognition system a unified pipeline is needed. The input in such system should be plain images of subjects and the output identities based only on ear biometrics. We conduct separate analysis through detection and identification experiments on the challenging dataset and, using the best approaches, present a novel, unified pipeline. The pipeline is based on convolutional neural networks (CNN) and presents, to the best of our knowledge, the first CNN-based ear recognition pipeline. The pipeline incorporates both, the detection of ears on arbitrary images of people, as well as recognition on these segmented ear regions. The experiments show that the presented system is a state-of-the-art system and, thus, a good foundation for future real-word ear recognition systems. |
Križaj, Janez; Perš, Janez; Dobrišek, Simon; Štruc, Vitomir Sistem nadgrajene resničnosti za verifikacijo predmetov v skladiščnih okoljih Članek v strokovni reviji V: Elektrotehniski Vestnik, vol. 86, no. 1/2, str. 1–6, 2019. Povzetek | Povezava | BibTeX | Oznake: augmented reality, IoT, smart sensors, wearables @article{krivzaj2019sistem,The paper proposes an augmented reality system for visual object verification that helps warehouse workers perform their work. The system sequentially captures images of objects that the warehouse workers encounter during their work and verifies whether the objects are the ones that the workers are supposed to fetch from storage. The system uses Android-powered smart glasses to capture image data and display results to the user, whereas the computationally-intensive verification task is carried out in the cloud and is implemented using recent deep-learning techniques. By doing so, the system is able to process images in near real-time and achieves a high verification accuracy as shown by the experimental results |
2018 |
Križaj, Janez; Emeršič, Žiga; Dobrišek, Simon; Peer, Peter; Štruc, Vitomir Localization of Facial Landmarks in Depth Images Using Gated Multiple Ridge Descent Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–8, IEEE 2018. Povzetek | Povezava | BibTeX | Oznake: 3d face, 3d landmarking, face alignment, face landmarking, gated ridge descent @inproceedings{krivzaj2018localization,A novel method for automatic facial landmark localization is presented. The method builds on the supervised descent framework, which was shown to successfully localize landmarks in the presence of large expression variations and mild occlusions, but struggles when localizing landmarks on faces with large pose variations. We propose an extension of the supervised descent framework that trains multiple descent maps and results in increased robustness to pose variations. The performance of the proposed method is demonstrated on the Bosphorus, the FRGC and the UND data sets for the problem of facial landmark localization from 3D data. Our experimental results show that the proposed method exhibits increased robustness to pose variations, while retaining high performance in the case of expression and occlusion variations. |
Kristan, Matej; Leonardis, Ales; Matas, Jiri; Felsberg, Michael; Pflugfelder, Roman; Zajc, Luka Cehovin; Vojir, Tomas; Bhat, Goutam; Lukezic, Alan; Eldesokey, Abdelrahman; Štruc, Vitomir; Grm, Klemen; others, The sixth visual object tracking VOT2018 challenge results Proceedings Article V: European Conference on Computer Vision Workshops (ECCV-W 2018), 2018. Povzetek | Povezava | BibTeX | Oznake: benchmark, tracking, VOT @inproceedings{kristan2018sixth,The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website. |
Rot, Peter; Emeršič, Žiga; Struc, Vitomir; Peer, Peter Deep multi-class eye segmentation for ocular biometrics Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–8, IEEE 2018. Povzetek | Povezava | BibTeX | Oznake: biometrics, eye, ocular, sclera, segmentation @inproceedings{rot2018deep,Segmentation techniques for ocular biometrics typically focus on finding a single eye region in the input image at the time. Only limited work has been done on multi-class eye segmentation despite a number of obvious advantages. In this paper we address this gap and present a deep multi-class eye segmentation model build around the SegNet architecture. We train the model on a small dataset (of 120 samples) of eye images and observe it to generalize well to unseen images and to ensure highly accurate segmentation results. We evaluate the model on the Multi-Angle Sclera Database (MASD) dataset and describe comprehensive experiments focusing on: i) segmentation performance, ii) error analysis, iii) the sensitivity of the model to changes in view direction, and iv) comparisons with competing single-class techniques. Our results show that the proposed model is viable solution for multi-class eye segmentation suitable for recognition (multi-biometric) pipelines based on ocular characteristics. |
Lozej, Juš; Meden, Blaž; Struc, Vitomir; Peer, Peter End-to-end iris segmentation using U-Net Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–6, IEEE 2018. Povzetek | Povezava | BibTeX | Oznake: biometrics, CNN, convolutional neural networks, iris, ocular, U-net @inproceedings{lozej2018end,Iris segmentation is an important research topic that received significant attention from the research community over the years. Traditional iris segmentation techniques have typically been focused on hand-crafted procedures that, nonetheless, achieved remarkable segmentation performance even with images captured in difficult settings. With the success of deep-learning models, researchers are increasingly looking towards convolutional neural networks (CNNs) to further improve on the accuracy of existing iris segmentation techniques and several CNN-based techniques have already been presented recently in the literature. In this paper we also consider deep-learning models for iris segmentation and present an iris segmentation approach based on the popular U-Net architecture. Our model is trainable end-to-end and, hence, avoids the need for hand designing the segmentation procedure. We evaluate the model on the CASIA dataset and report encouraging results in comparison to existing techniques used in this area. |
Meden, Blaz; Peer, Peter; Struc, Vitomir Selective Face Deidentification with End-to-End Perceptual Loss Learning Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–7, IEEE 2018. Povzetek | Povezava | BibTeX | Oznake: deidentification, face, face deidentification, privacy protection @inproceedings{meden2018selective,Privacy is a highly debatable topic in the modern technological era. With the advent of massive video and image data (which in a lot of cases contains personal information on the recorded subjects), there is an imminent need for efficient privacy protection mechanisms. To this end, we develop in this work a novel Face Deidentification Network (FaDeNet) that is able to alter the input faces in such a way that automated recognition fail to recognize the subjects in the images, while this is still possible for human observers. FaDeNet is based an encoder-decoder architecture that is trained to auto-encode the input image, while (at the same time) minimizing the recognition performance of a secondary network that is used as an socalled identity critic in FaDeNet. We present experiments on the Radbound Faces Dataset and observe encouraging results. |
Grm, Klemen; Štruc, Vitomir Deep face recognition for surveillance applications Članek v strokovni reviji V: IEEE Intelligent Systems, vol. 33, no. 3, str. 46–50, 2018. Povzetek | Povezava | BibTeX | Oznake: biometrics, face, face recognition, performance evaluation, surveillance @article{GrmIEEE2018,Automated person recognition from surveillance quality footage is an open research problem with many potential application areas. In this paper, we aim at addressing this problem by presenting a face recognition approach tailored towards surveillance applications. The presented approach is based on domain-adapted convolutional neural networks and ranked second in the International Challenge on Biometric Recognition in the Wild (ICB-RW) 2016. We evaluate the performance of the presented approach on part of the Quis-Campi dataset and compare it against several existing face recognition techniques and one (state-of-the-art) commercial system. We find that the domain-adapted convolutional network outperforms all other assessed techniques, but is still inferior to human performance. |
Emeršič, Žiga; Meden, Blaž; Peer, Peter; Štruc, Vitomir Evaluation and analysis of ear recognition models: performance, complexity and resource requirements Članek v strokovni reviji V: Neural Computing and Applications, str. 1–16, 2018, ISBN: 0941-0643. Povzetek | Povezava | BibTeX | Oznake: AWE, AWEx, descriptor methods, ear recognition, extended annotated web ears dataset @article{emervsivc2018evaluation,Ear recognition technology has long been dominated by (local) descriptor-based techniques due to their formidable recognition performance and robustness to various sources of image variability. While deep-learning-based techniques have started to appear in this field only recently, they have already shown potential for further boosting the performance of ear recognition technology and dethroning descriptor-based methods as the current state of the art. However, while recognition performance is often the key factor when selecting recognition models for biometric technology, it is equally important that the behavior of the models is understood and their sensitivity to different covariates is known and well explored. Other factors, such as the train- and test-time complexity or resource requirements, are also paramount and need to be consider when designing recognition systems. To explore these issues, we present in this paper a comprehensive analysis of several descriptor- and deep-learning-based techniques for ear recognition. Our goal is to discover weak points of contemporary techniques, study the characteristics of the existing technology and identify open problems worth exploring in the future. We conduct our analysis through identification experiments on the challenging Annotated Web Ears (AWE) dataset and report our findings. The results of our analysis show that the presence of accessories and high degrees of head movement significantly impacts the identification performance of all types of recognition models, whereas mild degrees of the listed factors and other covariates such as gender and ethnicity impact the identification performance only to a limited extent. From a test-time-complexity point of view, the results suggest that lightweight deep models can be equally fast as descriptor-based methods given appropriate computing hardware, but require significantly more resources during training, where descriptor-based methods have a clear advantage. As an additional contribution, we also introduce a novel dataset of ear images, called AWE Extended (AWEx), which we collected from the web for the training of the deep models used in our experiments. AWEx contains 4104 images of 346 subjects and represents one of the largest and most challenging (publicly available) datasets of unconstrained ear images at the disposal of the research community. |
Banerjee, Sandipan; Brogan, Joel; Krizaj, Janez; Bharati, Aparna; RichardWebster, Brandon; Struc, Vitomir; Flynn, Patrick J.; Scheirer, Walter J. To frontalize or not to frontalize: Do we really need elaborate pre-processing to improve face recognition? Proceedings Article V: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), str. 20–29, IEEE 2018. Povzetek | Povezava | BibTeX | Oznake: face alignment, face recognition, landmarking @inproceedings{banerjee2018frontalize,Face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional neural networks (CNNs). While CNNs have pushed the state-of-the-art forward, their training process requires a large amount of clean and correctly labelled training data. If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step? To address this question, we evaluate a number of facial landmarking algorithms and a popular frontalization method to understand their effect on facial recognition performance. Additionally, we introduce a new, automatic, single-image frontalization scheme that exceeds the performance of the reference frontalization algorithm for video-to-video face matching on the Point and Shoot Challenge (PaSC) dataset. Additionally, we investigate failure modes of each frontalization method on different facial yaw using the CMU Multi-PIE dataset. We assert that the subsequent recognition and verification performance serves to quantify the effectiveness of each pose correction scheme. |
Emeršič, Žiga; Gabriel, Luka; Štruc, Vitomir; Peer, Peter Convolutional encoder--decoder networks for pixel-wise ear detection and segmentation Članek v strokovni reviji V: IET Biometrics, vol. 7, no. 3, str. 175–184, 2018. Povzetek | Povezava | BibTeX | Oznake: annotated web ears, AWE, biometrics, ear, ear detection, pixel-wise detection, segmentation @article{emervsivc2018convolutional,Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detection, are commonly susceptible to the presence of severe occlusions, ear accessories or variable illumination conditions and often deteriorate in their performance if applied on ear images captured in unconstrained settings. To address these shortcomings, we present a novel ear detection technique based on convolutional encoder-decoder networks (CEDs). We formulate the problem of ear detection as a two-class segmentation problem and design and train a CED-network architecture to distinguish between image-pixels belonging to the ear and the non-ear class. Unlike competing techniques, our approach does not simply return a bounding box around the detected ear, but provides detailed, pixel-wise information about the location of the ears in the image. Experiments on a dataset gathered from the web (a.k.a. in the wild) show that the proposed technique ensures good detection results in the presence of various covariate factors and significantly outperforms competing methods from the literature. |
Emeršič, Žiga; Playa, Nil Oleart; Štruc, Vitomir; Peer, Peter Towards Accessories-Aware Ear Recognition Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–8, IEEE 2018. Povzetek | Povezava | BibTeX | Oznake: accessories, biometrics, ear recognition @inproceedings{emervsivc2018towards,Automatic ear recognition is gaining popularity within the research community due to numerous desirable properties, such as high recognition performance, the possibility of capturing ear images at a distance and in a covert manner, etc. Despite this popularity and the corresponding research effort that is being directed towards ear recognition technology, open problems still remain. One of the most important issues stopping ear recognition systems from being widely available are ear occlusions and accessories. Ear accessories not only mask biometric features and by this reduce the overall recognition performance, but also introduce new non-biometric features that can be exploited for spoofing purposes. Ignoring ear accessories during recognition can, therefore, present a security threat to ear recognition and also adversely affect performance. Despite the importance of this topic there has been, to the best of our knowledge, no ear recognition studies that would address these problems. In this work we try to close this gap and study the impact of ear accessories on the recognition performance of several state-of-the-art ear recognition techniques. We consider ear accessories as a tool for spoofing attacks and show that CNN-based recognition approaches are more susceptible to spoofing attacks than traditional descriptor-based approaches. Furthermore, we demonstrate that using inpainting techniques or average coloring can mitigate the problems caused by ear accessories and slightly outperforms (standard) black color to mask ear accessories. |
Objave
2022 |
Reconstructing Superquadrics from Intensity and Color Images Članek v strokovni reviji V: Sensors, vol. 22, iss. 4, no. 5332, 2022. |
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration Proceedings Article V: IAPR International Conference on Pattern Recognition (ICPR), 2022. |
Assessing Bias in Face Image Quality Assessment Proceedings Article V: EUSIPCO 2022, 2022. |
An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement Članek v strokovni reviji V: IEEE Transactions on Biometrics, Identity and Behavior (TBIOM), vol. 4, iss. 2, str. 263-275, 2022. |
Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models Proceedings Article V: International Workshop on Biometrics and Forensics (IWBF), str. 1–6, 2022. |
Segmentacija telesa z uporabo večciljnega učenja Proceedings Article V: Proceedings of Rosus 2022, 2022. |
Making the most of single sensor information : a novel fusion approach for 3D face recognition using region covariance descriptors and Gaussian mixture models Članek v strokovni reviji V: Sensors, iss. 6, no. 2388, str. 1-26, 2022. |
Body Segmentation Using Multi-task Learning Proceedings Article V: International Conference on Artificial Intelligence in Information and Communication (ICAIIC), IEEE, 2022, ISBN: 978-1-6654-5818-4. |
C-VTON: Context-Driven Image-Based Virtual Try-On Network Proceedings Article V: IEEE/CVF Winter Applications in Computer Vision (WACV), str. 1–10, 2022. |
Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition Članek v strokovni reviji V: Sensors, vol. 22, no. 1, str. 1-26, 2022. |
Detecting Soft-Biometric Privacy Enhancement Book Section V: Rathgeb, Christian; Tolosana, Ruben; Vera-Rodriguez, Ruben; Busch, Christoph (Ur.): Handbook of Digital Face Manipulation and Detection, 2022. |
Future Trends in Digital Face Manipulation and Detection Book Section V: Rathgeb, Christian; Tolosana, Ruben; Vera-Rodriguez, Ruben; Busch, Christoph (Ur.): Handbook of Digital Face Manipulation and Detection, str. 463–482, 2022, ISBN: 978-3-030-87663-0. |
2021 |
ContexedNet : Context-Aware Ear Detection in Unconstrained Settings Članek v strokovni reviji V: IEEE Access, str. 1–17, 2021, ISSN: 2169-3536. |
A Comparative Study on Discriminative and One--Class Learning Models for Deepfake Detection Proceedings Article V: Proceedings of ERK 2021, str. 1–4, 2021. |
Frequency Band Encoding for Face Super-Resolution Proceedings Article V: Proceedings of ERK 2021, str. 1-4, 2021. |
MFR 2021: Masked Face Recognition Competition Proceedings Article V: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. |
NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization Proceedings Article V: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021. |
Privacy-Enhancing Face Biometrics: A Comprehensive Survey Članek v strokovni reviji V: IEEE Transactions on Information Forensics and Security, vol. 16, str. 4147-4183, 2021. |
Benchmarking Crowd-Counting Techniques across Image Characteristics Članek v strokovni reviji V: Elektorethniski Vestnik, vol. 88, iss. 5, str. 227-235, 2021. |
How to correctly detect face-masks for COVID-19 from visual information? Članek v strokovni reviji V: Applied sciences, vol. 11, no. 5, str. 1-24, 2021, ISBN: 2076-3417. |
Learning to predict superquadric parameters from depth images with explicit and implicit supervision Članek v strokovni reviji V: IEEE Access, str. 1-16, 2021, ISSN: 2169-3536. |
High Resolution Face Editing with Masked GAN Latent Code Optimization Članek v strokovni reviji V: CoRR, vol. abs/2103.11135, 2021. |
2020 |
Learning privacy-enhancing face representations through feature disentanglement Proceedings Article V: Proceedings of FG 2020, IEEE, 2020. |
SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment Proceedings Article V: International Joint Conference on Biometrics (IJCB 2020), str. 1–10, 2020. |
Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies Proceedings Article V: Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG) 2020, str. 1-5, IEEE, 2020, ISSN: 1617-5468. |
Analysis of Race and Gender Bias in Deep Age Estimation Model Proceedings Article V: Proceedings of EUSIPCO 2020, 2020. |
PE-MIU: a training-free privacy-enhancing face recognition approach based on minimum information units Članek v strokovni reviji V: IEEE Access, vol. 2020, 2020. |
Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks Proceedings Article V: 25th Computer Vision Winter Workshop (CVWW 2020), 2020. |
Constellation-Based Deep Ear Recognition Book Section V: Jiang, R.; Li, CT.; Crookes, D.; Meng, W.; Rosenberger, C. (Ur.): Deep Biometrics: Unsupervised and Semi-Supervised Learning, Springer, 2020, ISBN: 978-3-030-32582-4. |
Face hallucination using cascaded super-resolution and identity priors Članek v strokovni reviji V: IEEE Transactions on Image Processing, 2020. |
A comprehensive investigation into sclera biometrics: a novel dataset and performance study Članek v strokovni reviji V: Neural Computing and Applications, str. 1-15, 2020. |
2019 |
Deep Sclera Segmentation and Recognition Book Section V: Uhl, Andreas; Busch, Christoph; Marcel, Sebastien; Veldhuis, Rainer (Ur.): Handbook of Vascular Biometrics, str. 395-432, Springer, 2019, ISBN: 978-3-030-27731-4. |
Simultaneous multi-decent regression and feature learning for landmarking in depth image Članek v strokovni reviji V: Neural Computing and Applications, 2019, ISBN: 0941-0643. |
Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study Proceedings Article V: 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 45-52, IEEE, 2019. |
The Unconstrained Ear Recognition Challenge 2019 Proceedings Article V: International Conference on Biometrics (ICB 2019), 2019. |
Face Hallucination Revisited: An Exploratory Study on Dataset Bias Proceedings Article V: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019. |
Frame-based classification for cross-speed gait recognition Članek v strokovni reviji V: Multimedia Tools and Applications, vol. 78, no. 5, str. 5621–5643, 2019, ISSN: 1573-7721. |
Influence of segmentation on deep iris recognition performance Proceedings Article V: 7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF 2019), 2019. |
Deep ear recognition pipeline Book Section V: Mahmoud, Hassaballah; M., Hosny Khalid (Ur.): Recent advances in computer vision : theories and applications, vol. 804, Springer, 2019, ISBN: 1860-9503. |
Sistem nadgrajene resničnosti za verifikacijo predmetov v skladiščnih okoljih Članek v strokovni reviji V: Elektrotehniski Vestnik, vol. 86, no. 1/2, str. 1–6, 2019. |
2018 |
Localization of Facial Landmarks in Depth Images Using Gated Multiple Ridge Descent Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–8, IEEE 2018. |
The sixth visual object tracking VOT2018 challenge results Proceedings Article V: European Conference on Computer Vision Workshops (ECCV-W 2018), 2018. |
Deep multi-class eye segmentation for ocular biometrics Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–8, IEEE 2018. |
End-to-end iris segmentation using U-Net Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–6, IEEE 2018. |
Selective Face Deidentification with End-to-End Perceptual Loss Learning Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–7, IEEE 2018. |
Deep face recognition for surveillance applications Članek v strokovni reviji V: IEEE Intelligent Systems, vol. 33, no. 3, str. 46–50, 2018. |
Evaluation and analysis of ear recognition models: performance, complexity and resource requirements Članek v strokovni reviji V: Neural Computing and Applications, str. 1–16, 2018, ISBN: 0941-0643. |
To frontalize or not to frontalize: Do we really need elaborate pre-processing to improve face recognition? Proceedings Article V: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), str. 20–29, IEEE 2018. |
Convolutional encoder--decoder networks for pixel-wise ear detection and segmentation Članek v strokovni reviji V: IET Biometrics, vol. 7, no. 3, str. 175–184, 2018. |
Towards Accessories-Aware Ear Recognition Proceedings Article V: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), str. 1–8, IEEE 2018. |