PT Unknown
AU Ajian Liu
   Jun Wan
   Sergio Escalera
   Hugo Jair Escalante
   Zichang Tan
   Qi Yuan
   Kai Wang
   Chi Lin
   Guodong Guo
   Isabelle Guyon
   Stan Z. Li
TI Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019
BT IEEE International Conference on Computer Vision and Pattern Recognition-Workshop
PY 2019
AB 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.
ER