%0 Conference Proceedings
%T Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019
%A Ajian Liu
%A Jun Wan
%A Sergio Escalera
%A Hugo Jair Escalante
%A Zichang Tan
%A Qi Yuan
%A Kai Wang
%A Chi Lin
%A Guodong Guo
%A Isabelle Guyon
%A Stan Z. Li
%B IEEE International Conference on Computer Vision and Pattern Recognition-Workshop
%D 2019
%F Ajian Liu2019
%O HuPBA; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3329), last updated on Fri, 21 Apr 2023 16:00:29 +0200
%X Anti-spoofing attack detection is critical to guarantee the security of face-based authentication and facial analysis systems. Recently, a multi-modal face anti-spoofing dataset, CASIA-SURF, has been released with the goal of boosting research in this important topic. CASIA-SURF is the largest public data set for facial anti-spoofing attack detection in terms of both, diversity and modalities: it comprises 1,000 subjects and 21,000 video samples. We organized a challenge around this novel resource to boost research in the subject. The Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions.
%U http://refbase.cvc.uab.es/files/LWE2019.pdf