Face Liveness Detection Competition (LivDet-Face)

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IEEE International Joint Conference on Biometrics Competitions, 2024

Authors:

Lambert Igene, Afzal Hossain, Stephanie Schuckers, Mohammad Zahir Uddin Chowdhury, Humaira Rezaie, Ayden Rollins, Jesse Dykes, Rahul Vijaykumar, Sebastien Marcel, Juan Tapia, Carlos Aravena, Daniel Schulz, Nima Karimian and Anafsheh Adami, Diogo Nunes, João Marcos, Nuno Gonçalves, Lovro Sikošek, Borut Batagelj, Nima Schei, David Pabon, Manuela Tiedemann, Vasiliy Pryadchenko, Aleksandr Alenin, Alhasan Alkhaddour, Anton Pimenov, Artem Tregubov, Igor Avdonin, Maxim Lazantsev and Mikhail Pozigun

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%.