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

Das, Priyanka and Mcfiratht, Joseph and Fang, Zhaoyuan and Boyd, Aidan and Jang, Ganghee and Mohammadi, Amir and Purnapatra, Sandip and Yambay, David and Marcel, Sebastien and Trokielewicz, Mateusz and Maciejewicz, Piotr and Bowyer, Kevin and Czajka, Adam and Schuckers, Stephanie and Tapia, Juan and Gonzalez, Sebastian and Fang, Meiling and Damer, Naser and Boutros, Fadi and Kuijper, Arian and Sharma, Renu and Chen, Cunjian and Ross, Arun (2020):
Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition.
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/IJCB48548.2020.9304941,
[Conference or Workshop Item]

Abstract

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.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Das, Priyanka and Mcfiratht, Joseph and Fang, Zhaoyuan and Boyd, Aidan and Jang, Ganghee and Mohammadi, Amir and Purnapatra, Sandip and Yambay, David and Marcel, Sebastien and Trokielewicz, Mateusz and Maciejewicz, Piotr and Bowyer, Kevin and Czajka, Adam and Schuckers, Stephanie and Tapia, Juan and Gonzalez, Sebastian and Fang, Meiling and Damer, Naser and Boutros, Fadi and Kuijper, Arian and Sharma, Renu and Chen, Cunjian and Ross, Arun
Title: Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition
Language: English
Abstract:

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.

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 08:06
DOI: 10.1109/IJCB48548.2020.9304941
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