Multi-Modal Face Presentation Attack Detection - Wan, Jun

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Résumé :
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.

Biographie:
Jun Wan received a B.S. degree from the China University of Geosciences, Beijing, China, in 2008, and a Ph.D. degree from the Institute of Information Science, Beijing Jiaotong University, Beijing, China, in 2015. Since January 2015, he has been a Faculty Member with the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Science (CASIA), China, where he currently serves as an Associate Professor. He is an IEEE Senior Member and a director of Chalearn Challenges. He has published more than 50 research papers and has been guest editor at TPAMI, MVA, and Entropy. His main research interests include computer vision, machine learning, especially for face and pedestrian analysis (such as attribute analysis, face anti-spoofing detection), gesture and sign language recognition. He has published papers in top journals and conferences, such as JMLR, T-PAMI, T-IP, T-MM, T-CYB, TOMM, PR, CVIU, CVPR, AAAI, and IJCAI. He has served as the reviewer on several journals and conferences, such as JMLR, T-PAMI, T-IP, T-MM, T-SMC, PR, CVPR, ICCV, ECCV, AAAI, and ICRA.