Publications

@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{TIFS_Sclera2022,

title = {Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach},

author = {Matej Vitek and Abhijit Das and Diego Rafael Lucio and Luiz Antonio Zanlorensi Jr. and David Menotti and Jalil Nourmohammadi Khiarak and Mohsen Akbari Shahpar and Meysam Asgari-Chenaghlu and Farhang Jaryani and Juan E. Tapia and Andres Valenzuela and Caiyong Wang and Yunlong Wang and Zhaofeng He and Zhenan Sun and Fadi Boutros and Naser Damer and Jonas Henry Grebe and Arjan Kuijper and Kiran Raja and Gourav Gupta and Georgios Zampoukis and Lazaros Tsochatzidis and Ioannis Pratikakis and S. V. Aruna Kumar and B. S. Harish and Umapada Pal and Peter Peer and Vitomir Štruc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926136},

doi = {10.1109/TIFS.2022.3216468},

issn = {1556-6013},

year  = {2023},

date = {2023-01-18},

urldate = {2022-10-18},

journal = {IEEE Transactions on Information Forensics and Security},

volume = {18},

pages = {190-205},

abstract = {Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.},

keywords = {bias, biometrics, fairness, group evaluation, ocular, sclera, sclera segmentation, segmentation},

pubstate = {published},

tppubtype = {article}

}