Das, Priyanka ; Mcfiratht, Joseph ; Fang, Zhaoyuan ; Boyd, Aidan ; Jang, Ganghee ; Mohammadi, Amir ; Purnapatra, Sandip ; Yambay, David ; Marcel, Sebastien ; Trokielewicz, Mateusz ; Maciejewicz, Piotr ; Bowyer, Kevin ; Czajka, Adam ; Schuckers, Stephanie ; Tapia, Juan ; Gonzalez, Sebastian ; Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kuijper, Arian ; Sharma, Renu ; Chen, Cunjian ; Ross, Arun (2020)
Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition.
2020 IEEE International Joint Conference on Biometrics (IJCB). virtual Conference (28.09.-01.10.2020)
doi: 10.1109/IJCB48548.2020.9304941
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Das, Priyanka ; Mcfiratht, Joseph ; Fang, Zhaoyuan ; Boyd, Aidan ; Jang, Ganghee ; Mohammadi, Amir ; Purnapatra, Sandip ; Yambay, David ; Marcel, Sebastien ; Trokielewicz, Mateusz ; Maciejewicz, Piotr ; Bowyer, Kevin ; Czajka, Adam ; Schuckers, Stephanie ; Tapia, Juan ; Gonzalez, Sebastian ; Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kuijper, Arian ; Sharma, Renu ; Chen, Cunjian ; Ross, Arun |
| Art des Eintrags: | Bibliographie |
| Titel: | Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 2020 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 28.09.-01.10.2020 |
| DOI: | 10.1109/IJCB48548.2020.9304941 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Machine learning, Artificial intelligence (AI), Iris recognition, Spoofing attacks |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 01 Feb 2021 08:06 |
| Letzte Änderung: | 01 Feb 2021 08:06 |
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