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Micro Stripes Analyses for Iris Presentation Attack Detection

: Fang, Meiling; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan


Kakadiaris, Ioannis A. (General Chairs) ; Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Biometrics Council:
IEEE International Joint Conference on Biometrics, IJCB 2020 : 28 Sept.-1 Oct. 2020, Houston, Texas, Online
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-9186-7
ISBN: 978-1-7281-9187-4
10 pp.
International Joint Conference on Biometrics (IJCB) <2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Conference Paper
Fraunhofer IGD ()
ATHENE; CRISP; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); biometrics; machine learning; artificial intelligence (AI); Iris recognition; spoofing attacks

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.