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Micro Stripes Analyses for Iris Presentation Attack Detection

Fang, Meiling and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Micro Stripes Analyses for Iris Presentation Attack Detection.
IEEE, 2020 IEEE International Joint Conference on Biometrics (IJCB), virtual Conference, 28.09.-01.10.2020, ISBN 978-1-7281-9186-7,
DOI: 10.1109/IJCB48548.2020.9304886,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Fang, Meiling and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan
Title: Micro Stripes Analyses for Iris Presentation Attack Detection
Language: English
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.

Publisher: IEEE
ISBN: 978-1-7281-9186-7
Uncontrolled Keywords: Biometrics, Machine learning, Artificial intelligence (AI), Iris recognition, Spoofing attacks
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 2020 IEEE International Joint Conference on Biometrics (IJCB)
Event Location: virtual Conference
Event Dates: 28.09.-01.10.2020
Date Deposited: 01 Feb 2021 08:08
DOI: 10.1109/IJCB48548.2020.9304886
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