With the wide deployment of face recognition systems, there are increasing varieties of attacks to the face biometric systems. They include physical attacks such as face spoofing, as well as digital attacks such as adversarial attack and digital manipulation. How to develop effective detection algorithms to defend against these various attacks is becoming an emerging topic in biometrics. This talk will survey the recent efforts in developing detection algorithms for all three types of attacks, and discuss some of the future research directions.
Dr. Xiaoming Liu is a Professor at the Department of Computer Science and Engineering of Michigan State University (MSU). He received Ph.D. degree from Carnegie Mellon University in 2004. Before joining MSU in 2012, he was a research scientist at General Electric (GE) Global Research. He works on computer vision, machine learning, and biometrics, especially on face related analysis. Since 2012, he helps to develop a strong computer vision area in MSU, who is ranked top 15 nationally according to the 5-year statistics at csrankings.org. He received the 2018 Withrow Distinguished Scholar Award from MSU. He has been Area Chairs for numerous conferences, including CVPR, ICCV, ECCV, ICLR, NeurIPS, ICML, the Co-Program Chair of BTAS’18, WACV’18, and AVSS’21 conferences, and Co-General Chair of FG’23 conference. He is an Associate Editor of Pattern Recognition Letters, Pattern Recognition, and IEEE Transaction on Image Processing. He has authored more than 150 scientific publications, and has filed 28 U.S. patents. His work has been cited over 10,000 times according to Google Scholar, with an H-index of 52. He is a fellow of IAPR.