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  4. Micro Stripes Analyses for Iris Presentation Attack Detection
 
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2020
Conference Paper
Titel

Micro Stripes Analyses for Iris Presentation Attack Detection

Abstract
Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segmentation is modified. For our Presentation Attack Detection (PAD) network to better model the classification problem, the segmented area is processed to provide lower dimensional input segments and a higher number of learning samples. Our proposed Micro Stripes Analyses (MSA) solution samples the segmented areas as individual stripes. Then, the majority vote makes the final classification decision of those micro-stripes. Experiments are demonstrated on five databases, where two databases (IIITD-WVU and Notre Dame) are from the LivDet-2017 Iris competition. An in-depth experimental evaluation of this framework reveals a superior performance compared with state-of-the-art (SoTA) algorithms. Moreover, our solution minimizes the confusion between textured (attack) and soft (bona fide) contact lens presentations.
Author(s)
Fang, Meiling
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Damer, Naser
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kirchbuchner, Florian
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Kuijper, Arjan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Hauptwerk
IEEE International Joint Conference on Biometrics, IJCB 2020
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Joint Conference on Biometrics (IJCB) 2020
Thumbnail Image
DOI
10.1109/IJCB48548.2020.9304886
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Tags
  • ATHENE

  • CRISP

  • Lead Topic: Visual Co...

  • Research Line: Comput...

  • biometrics

  • machine learning

  • artificial intelligen...

  • Iris recognition

  • spoofing attacks

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