Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019

Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, Zichang Tan, Qi Yuan, Kai Wang, Chi Lin, Guodong Guo, Isabelle Guyon, Stan Z. Li; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


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.

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[bibtex]
@InProceedings{Liu_2019_CVPR_Workshops,
author = {Liu, Ajian and Wan, Jun and Escalera, Sergio and Jair Escalante, Hugo and Tan, Zichang and Yuan, Qi and Wang, Kai and Lin, Chi and Guo, Guodong and Guyon, Isabelle and Li, Stan Z.},
title = {Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}