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Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition

: Das, Priyanka; McGrath, Joseph; Fang, Zhaoyuan; Boyd, Aidan; Jang, Ganghee; Mohammadi, Amir; Purnapatra, Sandip; Yambay, David; Marcel, Sébastien; Trokielewicz, Mateusz; Maciejewicz, Piotr; Bowyer, Kevin W.; Czajka, Adam; Schuckers, Stephanie; Tapia, Juan; Gonzalez, Sebastian; Fang, Meiling; Damer, Naser; Boutros, Fadi; Kuijper, Arjan; Sharma, Renu; Chen, Cunjian; Ross, Arun A.


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
9 pp.
International Joint Conference on Biometrics (IJCB) <2020, Online>
National Science Foundation NSF
Conference Paper
Fraunhofer IGD ()
Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); biometrics; Research Line- Machine Learning (ML); artificial intelligence (AI); Iris recognition; spoofing attacks; ATHENE; CRISP

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year's competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)* open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10% and a BPCER of 0.46% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments.