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  4. Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition
 
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2020
Conference Paper
Title

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

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.
Author(s)
Das, Priyanka
Clarkson Univ.
McGrath, Joseph
Univ. of Notre Dame
Fang, Zhaoyuan
Univ. of Notre Dame
Boyd, Aidan
Univ. of Notre Dame
Jang, Ganghee
Clarkson Univ.
Mohammadi, Amir
Idiap Research Institute
Purnapatra, Sandip
Clarkson Univ.
Yambay, David
Clarkson Univ.
Marcel, Sébastien
Idiap Research Institut
Trokielewicz, Mateusz
Warsaw Univ. of Technology
Maciejewicz, Piotr
Medical Univ. of Warsaw
Bowyer, Kevin W.
Univ. of Notre Dame
Czajka, Adam
Univ. of Notre Dame
Schuckers, Stephanie
Clarkson Univ.
Tapia, Juan
Univ. de Santiago
Gonzalez, Sebastian
TOC Biometrics - Chile
Fang, Meiling  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Boutros, Fadi  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Sharma, Renu
Michigan State Univ.
Chen, Cunjian
Michigan State Univ.
Ross, Arun A.
Michigan State Univ.
Mainwork
IEEE International Joint Conference on Biometrics, IJCB 2020  
Funder
National Science Foundation NSF  
Conference
International Joint Conference on Biometrics (IJCB) 2020  
Open Access
DOI
10.1109/IJCB48548.2020.9304941
Additional link
Full text
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • 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

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