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IEEE Transactions on Technology and Society 2022年第1期3卷 16-30页

作者： Terhorst, Philipp Kolf, Jan Niklas Huber, Marco Kirchbuchner, Florian Damer, Naser Moreno, Aythami Morales Fierrez, Julian Kuijper, Arjan Department of Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt64283 Germany Interactive Graphics Systems Group Technical University of Darmstadt Darmstadt64289 Germany Biometrics and Data Pattern Analytics Lab-BiDA Lab Universidad Autonoma de Madrid Madrid28049 Spain

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyze FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group-based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many nondemographic attributes strongly affect recognition performance, such as accessories, hairstyles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making the FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, enhance the robustness of these networks, and develop more generalized bias-mitigating FR solutions. © 2022 IEEE.

关键词： biometrics

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SELM: Siamese extreme learning machine with application to face biometrics

arXiv

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arXiv 2021年

作者： Kudisthalert, Wasu Pasupa, Kitsuchart Morales, Aythami Fierrez, Julian Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand Biometric and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

Extreme Learning Machine is a powerful classification method very competitive existing classification methods. It is extremely fast at training. Nevertheless, it cannot perform face verification tasks properly because face verification tasks require comparison of facial images of two individuals at the same time and decide whether the two faces identify the same person. The structure of Extreme Leaning Machine was not designed to feed two input data streams simultaneously, thus, in 2-input scenarios Extreme Learning Machine methods are normally applied using concatenated inputs. However, this setup consumes two times more computational resources and it is not optimized for recognition tasks where learning a separable distance metric is critical. For these reasons, we propose and develop a Siamese Extreme Learning Machine (SELM). SELM was designed to be fed with two data streams in parallel simultaneously. It utilizes a dual-stream Siamese condition in the extra Siamese layer to transform the data before passing it along to the hidden layer. Moreover, we propose a Gender-Ethnicity-Dependent triplet feature exclusively trained on a variety of specific demographic groups. This feature enables learning and extracting of useful facial features of each group. Experiments were conducted to evaluate and compare the performances of SELM, Extreme Learning Machine, and DCNN. The experimental results showed that the proposed feature was able to perform correct classification at 97.87% accuracy and 99.45% AUC. They also showed that using SELM in conjunction with the proposed feature provided 98.31% accuracy and 99.72% AUC. They outperformed the well-known DCNN and Extreme Leaning Machine methods by a wide margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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SensitiveNets: Learning agnostic representations with application to face recognition

arXiv

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arXiv 2019年

作者： Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Biometric and Data Pattern Analytics Lab-BiDA Escuela Politecnica Superior Universi-dad Autónoma de Madrid Campus de Cantoblanco C/ Francisco Tomás y Valiente 11 Madrid28049 Spain

This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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