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

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
Hauptwerk
28th European Signal Processing Conference, EUSIPCO 2020. Proceedings
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
European Signal Processing Conference (EUSIPCO) 2020
European Signal Processing Conference (EUSIPCO) 2021
Thumbnail Image
DOI
10.23919/Eusipco47968.2020.9287321
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • Lead Topic: Digitized Work

  • Lead Topic: Smart City

  • Research Line: Computer vision (CV)

  • biometrics

  • computer vision

  • machine learning

  • ATHENE

  • CRISP

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