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  4. Demographic Fairness in Biometric Systems: What Do the Experts Say?
 
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2022
Journal Article
Title

Demographic Fairness in Biometric Systems: What Do the Experts Say?

Abstract
Biometric technologies [1] have become an integral component of many personal, commercial, and governmental identity management systems worldwide. Biometrics rely on highly distinctive characteristics of human beings, which make it possible for individuals to be reliably recognized using fully automated algorithms. Prominent examples of biometric characteristics used for recognition purposes are face, fingerprint, iris, and voice. Application scenarios of biometrics beyond personal devices (see [2] ) include, but are not limited to, border control (see [3] , [4] , and [5] ), forensic investigations, law enforcement (see [6] and [7] ), and national ID systems (see [8] ).
Author(s)
Rathgeb, Christian
Hochschule Darmstadt  
Drozdowski, Pawel
Hochschule Darmstadt  
Frings, Dinusha C.
European Association for Biometrics -EAB-  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Busch, Christoph
Hochschule Darmstadt  
Journal
IEEE technology and society magazine  
Project(s)
Next Generation Biometric Systems  
Next Generation Biometric Systems  
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Hessisches Ministerium für Wissenschaft und Kunst
Open Access
DOI
10.1109/MTS.2022.3217700
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Digitized Work

  • Lead Topic: Smart City

  • Lead Topic: Visual Computing as a Service

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • Research Line: Machine Learning (ML)

  • Biometrics

  • Bias

  • Fairness

  • User study

  • Machine learning

  • ATHENE

  • CRISP

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