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Cited by 263Ethics and Social Impacts of AIBiometric Identification and SecurityFace recognition and analysis

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

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Drozdowski, Pawel, Rathgeb, Christian, Dantcheva, Antitza, Damer, Naser, Busch, Christoph·2020·IEEE Transactions on Technology and Society
DOI 10.1109/tts.2020.2992344
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Abstract

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as “racist” or “biased” by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics; 2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation; 3) a discussion of the pertinent technical and social matters; and 4) an outline of the remaining challenges and future work items, both from technological and social points of view.

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  • Conventional face recognition algorithms are biased in performance on different groups of genders, races or ages.

    “It is known that conventional face recognition algorithms are biased in performance on different groups of genders, races or ages [23].”

    From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [23]

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Cited by

Sorted by citations

Authors
Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., & Busch, C.
Affiliations
  • Darmstadt University of Applied Sciences (DE)
  • Centre Inria d'Université Côte d'Azur (FR)
  • Fraunhofer Institute for Computer Graphics Research (DE)
ORCID
Venue
IEEE Transactions on Technology and Society
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-06
Volume
1
Issue
2
Pages
89-103
ISSN
2637-6415 (ISSN-L)
DOI
10.1109/tts.2020.2992344
OpenAlex
W3009247505
Cited by
263
References
13
Citations by year
2026: 18 · 2025: 36 · 2024: 48 · 2023: 44 · 2022: 42 · 2021: 50 · 2020: 25
FWCI
54.83
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Technology and Society (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, cc-by)
  • TUbilio (Technical University of Darmstadt) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
  • Agence Nationale de la Recherche (ANR-18-CE92-0024, ANR-17-CE39-0002, ANR-17)
  • Bundesministerium für Bildung und Forschung
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords
algorithmic bias, biometrics, demographic bias, face recognition, racial bias, bias mitigation, bias estimation, identity management, automated decision systems

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