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Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Drozdowski, Pawel ; Rathgeb, Christian ; Dantcheva, Antitza ; Damer, Naser ; Busch, Christoph (2020)
Demographic Bias in Biometrics: A Survey on an Emerging Challenge.
In: IEEE Transactions on Technology and Society
doi: 10.1109/TTS.2020.2992344
Article, Bibliographie

Abstract

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g. access control) and non-cooperative (e.g. surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioural characteristics of human beings, which enable for individuals to be reliably recognised 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 labelled as “racist” or “biased” by the media, non-governmental organisations, 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.

Item Type: Article
Erschienen: 2020
Creators: Drozdowski, Pawel ; Rathgeb, Christian ; Dantcheva, Antitza ; Damer, Naser ; Busch, Christoph
Type of entry: Bibliographie
Title: Demographic Bias in Biometrics: A Survey on an Emerging Challenge
Language: English
Date: 2020
Journal or Publication Title: IEEE Transactions on Technology and Society
DOI: 10.1109/TTS.2020.2992344
URL / URN: https://ieeexplore.ieee.org/document/9086771
Abstract:

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g. access control) and non-cooperative (e.g. surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioural characteristics of human beings, which enable for individuals to be reliably recognised 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 labelled as “racist” or “biased” by the media, non-governmental organisations, 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.

Uncontrolled Keywords: Biometrics, Biometric identification systems, Biometric features
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 13 May 2020 07:06
Last Modified: 13 May 2020 07:06
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