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Demographic Bias in Biometrics: A Survey on an Emerging Challenge

 
: Drozdowski, Pawel; Rathgeb, Christian; Dantcheva, Antitza; Damer, Naser; Busch, Christoph

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Volltext urn:nbn:de:0011-n-5895997 (3.3 MByte PDF)
MD5 Fingerprint: 75778fdb9f06c93cb8fe1608280d1456
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Erstellt am: 13.5.2020


IEEE Transactions on Technology and Society (2020), Online First, 15 S.
ISSN: 2637-6415
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IGD ()
Lead Topic: Digitized Work; Lead Topic: Smart City; Research Line: Computer vision (CV); biometrics; biometric identification systems; Biometric features; ATHENE

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.

: http://publica.fraunhofer.de/dokumente/N-589599.html