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@article{PR_Fairness2024,

title = {Fairness in Face Presentation Attack Detection},

author = {Meiling Fang and Wufei Yang and Arjan Kuijper and Vitomir S̆truc and Naser Damer},

url = {https://www.sciencedirect.com/science/article/pii/S0031320323007008?dgcid=coauthor},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-01},

journal = {Pattern Recognition},

volume = {147 },

issue = {110002},

pages = {1-14},

abstract = {Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases.},

keywords = {biometrics, computer vision, face analysis, face PAD, face recognition, fairness, pad, presentation attack detection},

pubstate = {published},

tppubtype = {article}

}

@article{MaskFaceGAN,

title = {MaskFaceGAN: High Resolution Face Editing With Masked GAN Latent Code Optimization},

author = {Martin Pernuš and Vitomir Štruc and Simon Dobrišek},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10299582

https://lmi.fe.uni-lj.si/wp-content/uploads/2023/02/MaskFaceGAN_compressed.pdf

https://arxiv.org/pdf/2103.11135.pdf},

doi = {10.1109/TIP.2023.3326675},

issn = {1941-0042},

year  = {2023},

date = {2023-10-27},

urldate = {2023-01-02},

journal = {IEEE Transactions on Image Processing},

abstract = {Face editing represents a popular research topic within the computer vision and image processing communities. While significant progress has been made recently in this area, existing solutions: ( i ) are still largely focused on low-resolution images, ( ii ) often generate editing results with visual artefacts, or ( iii ) lack fine-grained control over the editing procedure and alter multiple (entangled) attributes simultaneously, when trying to generate the desired facial semantics. In this paper, we aim to address these issues through a novel editing approach, called MaskFaceGAN that focuses on local attribute editing. The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i.e., StyleGAN2) with respect to several constraints that ensure: ( i ) preservation of relevant image content, ( ii ) generation of the targeted facial attributes, and ( iii ) spatially–selective treatment of local image regions. The constraints are enforced with the help of an (differentiable) attribute classifier and face parser that provide the necessary reference information for the optimization procedure. MaskFaceGAN is evaluated in extensive experiments on the FRGC, SiblingsDB-HQf, and XM2VTS datasets and in comparison with several state-of-the-art techniques from the literature. Our experimental results show that the proposed approach is able to edit face images with respect to several local facial attributes with unprecedented image quality and at high-resolutions (1024×1024), while exhibiting considerably less problems with attribute entanglement than competing solutions. The source code is publicly available from: https://github.com/MartinPernus/MaskFaceGAN.},

keywords = {CNN, computer vision, deep learning, face editing, face image processing, GAN, GAN inversion, generative models, StyleGAN},

pubstate = {published},

tppubtype = {article}

}

@article{Batagelj2021,

title = {How to correctly detect face-masks for COVID-19 from visual information?},

author = {Borut Batagelj and Peter Peer and Vitomir Štruc and Simon Dobrišek},

url = {https://www.mdpi.com/2076-3417/11/5/2070/pdf},

doi = {10.3390/app11052070},

isbn = {2076-3417},

year  = {2021},

date = {2021-03-01},

urldate = {2021-03-01},

journal = {Applied sciences},

volume = {11},

number = {5},

pages = {1-24},

abstract = {The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and (iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.},

keywords = {computer vision, COVID-19, deep learning, detection, face, mask detection, recognition},

pubstate = {published},

tppubtype = {article}

}

@article{Oblak2021,

title = {Learning to predict superquadric parameters from depth images with explicit and implicit supervision},

author = {Tim Oblak and Jaka Šircelj and Vitomir Struc and Peter Peer and Franc Solina and Aleš Jaklic

},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9274424},

doi = {10.1109/ACCESS.2020.3041584},

issn = {2169-3536},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

pages = {1-16},

abstract = {Reconstruction of 3D space from visual data has always been a significant challenge in

the field of computer vision. A popular approach to address this problem can be found in the form of

bottom-up reconstruction techniques which try to model complex 3D scenes through a constellation of

volumetric primitives. Such techniques are inspired by the current understanding of the human visual

system and are, therefore, strongly related to the way humans process visual information, as suggested

by recent visual neuroscience literature. While advances have been made in recent years in the area of

3D reconstruction, the problem remains challenging due to the many possible ways of representing 3D

data, the ambiguity of determining the shape and general position in 3D space and the difficulty to train

efficient models for the prediction of volumetric primitives. In this paper, we address these challenges and

present a novel solution for recovering volumetric primitives from depth images. Specifically, we focus on

the recovery of superquadrics, a special type of parametric models able to describe a wide array of 3D

shapes using only a few parameters. We present a new learning objective that relies on the superquadric

(inside-outside) function and develop two learning strategies for training convolutional neural networks

(CNN) capable of predicting superquadric parameters. The first uses explicit supervision and penalizes the

difference between the predicted and reference superquadric parameters. The second strategy uses implicit

supervision and penalizes differences between the input depth images and depth images rendered from

the predicted parameters. CNN predictors for superquadric parameters are trained with both strategies and

evaluated on a large dataset of synthetic and real-world depth images. Experimental results show that both

strategies compare favourably to the existing state-of-the-art and result in high quality 3D reconstructions

of the modelled scenes at a much shorter processing time.},

keywords = {3d, computer vision, depth images, differential renderer, recovery, superquadric},

pubstate = {published},

tppubtype = {article}

}

@article{TIPKlemen_2020,

title = {Face hallucination using cascaded super-resolution and identity priors},

author = {Klemen Grm and Walter J. Scheirer and Vitomir Štruc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8866753

https://lmi.fe.uni-lj.si/wp-content/uploads/2023/02/IEEET_face_hallucination_compressed.pdf},

doi = {10.1109/TIP.2019.2945835},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Transactions on Image Processing},

abstract = {In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the lowresolution facial images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from most competing super-resolution techniques that rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of 2×. This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. The proposed C-SRIP model (Cascaded Super Resolution with Identity Priors) is able to upscale (tiny) low-resolution images captured in unconstrained conditions and produce visually convincing results for diverse low-resolution inputs. We rigorously evaluate the proposed model on the Labeled Faces in the Wild (LFW), Helen and CelebA datasets and report superior performance compared to the existing state-of-the-art.

},

keywords = {biometrics, CNN, computer vision, deep learning, face, face hallucination, super-resolution},

pubstate = {published},

tppubtype = {article}

}

@article{meden2017face,

title = {Face deidentification with generative deep neural networks},

author = {Blaž Meden and Refik Can Malli and Sebastjan Fabijan and Hazim Kemal Ekenel and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/Face_Deidentification_with_Generative_Deep_Neural_Networks.pdf},

year  = {2017},

date = {2017-01-01},

journal = {IET Signal Processing},

volume = {11},

number = {9},

pages = {1046--1054},

publisher = {IET},

abstract = {Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelisation have been replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and retain certain characteristics of the data even after deidentification. The latter aspect is important, as it allows the deidentified data to be used in applications for which identity information is irrelevant. In this work, the authors present a novel face deidentification pipeline, which ensures anonymity by synthesising artificial surrogate faces using generative neural networks (GNNs). The generated faces are used to deidentify subjects in images or videos, while preserving non-identity-related aspects of the data and consequently enabling data utilisation. Since generative networks are highly adaptive and can utilise diverse parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race etc.), they represent a natural choice for the problem of face deidentification. To demonstrate the feasibility of the authors’ approach, they perform experiments using automated recognition tools and human annotators. Their results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is effective.},

keywords = {biometrics, computer vision, deidentification, face, privacy protection},

pubstate = {published},

tppubtype = {article}

}