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  4. Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
 
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2020
Conference Paper
Title

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

Abstract
With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD- 2013 [18] database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.
Author(s)
Fang, Meiling  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kirchbuchner, Florian  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
28th European Signal Processing Conference, EUSIPCO 2020. Proceedings  
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
European Signal Processing Conference (EUSIPCO) 2020  
European Signal Processing Conference (EUSIPCO) 2021  
Open Access
DOI
10.23919/Eusipco47968.2020.9287321
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Digitized Work

  • Lead Topic: Smart City

  • Research Line: Computer vision (CV)

  • biometrics

  • computer vision

  • machine learning

  • ATHENE

  • CRISP

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