Face Liveness Detection Competition (LivDet-Face) - 2024

Imagine a world where a copy of your face could trick the most advanced security systems. This isn’t science fiction; it’s a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). This international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. Through the International Joint Conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. The competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year’s competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%.

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Publication Details

Published
2024-09-15
DOI
https://doi.org/10.1109/ijcb62174.2024.10744462
Citations
5
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
1.25

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article

Face Liveness Detection Competition (LivDet-Face) - 2024

Andrew M. Rollins, R. Vijaykumar, Nima Karimian, Sébastien Marcel et al.
5 citations
Face recognition and analysis
1.25
article

Face Liveness Detection Competition (LivDet-Face) - 2024

Andrew M. Rollins, R. Vijaykumar, Nima Karimian, Sébastien Marcel, Juan Tapia, Mikhail Pozigun, Miriam von Tiedemann, Ali Komaty, Stephanie Schuckers, Borut Batagelj, João Marcos, Anton Pimenov, Lambert Igene, Daniel Schulz, Morshed Chowdhury, Jason Dykes, А. С. Трегубов, Banafsheh Adami, Alhasan Alkhaddour, Vasiliy Pryadchenko, Denisse Carline Argüelles Pabón, Afzal Hossain, Nuno Gonçalves, Diogo Nunes, Humaira Rezaie, Carlos Aravena, Lovro Sikošek, Aleksandr Alenin, Igor Avdonin, Maxim Kazantsev, Nima Schei
article en
5 citations

Abstract

Imagine a world where a copy of your face could trick the most advanced security systems. This isn’t science fiction; it’s a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). This international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. Through the International Joint Conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. The competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year’s competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%.

Darmstadt University of Applied Sciences (DE), West Virginia University (US), University of Ljubljana (SI), Clarkson University (US), Institute for Systems Engineering and Computers (PT), Idiap Research Institute (CH), University of Coimbra (PT)
National Science Foundation
Reduced inequalities
Openalex Percentile: Top 18%
Face recognition and analysis
1.25
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