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  4. A Comprehensive Study on Face Recognition Biases Beyond Demographics
 
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2022
Journal Article
Titel

A Comprehensive Study on Face Recognition Biases Beyond Demographics

Abstract
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 analyse 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 non-demographic attributes strongly affect the recognition performance, such as accessories, hair-styles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, to enhance the robustness of these networks, and to develop more generalized bias-mitigating face recognition solutions.
Author(s)
Terhörst, Philipp
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kolf, Jan Niklas
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Huber, Marco
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kirchbuchner, Florian orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Morales, Aythami
Univ. Autonoma de Madrid
Fierrez, Julian
Univ. Autonoma de Madrid
Kuijper, Arjan orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Zeitschrift
IEEE Transactions on Technology and Society
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Thumbnail Image
DOI
10.1109/TTS.2021.3111823
Externer Link
Externer Link
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • ATHENE

  • CRISP

  • Lead Topic: Smart City

  • Research Line: Computer vision (CV)

  • Research Line: Machine Learning (ML)

  • face recognition

  • Bias

  • biometrics

  • machine learning

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