Colloquia / Seminars / Industry Talks

Deep learning models, especially CNN, has been successfully applied to face recognition, especially under the evaluation protocol of Labeled Faces in the Wild (LFW), when big face data is available. In this talk, except showing some recent results of CNN feature for video-based face processing (our FG’15 paper), I will also show that alternative deep models such as Auto-Encoder can also benefit face recognition impressively, especially for face alignment (our ECCV14 paper) and pose normalization (our CVPR14 paper) purpose. Both works might imply in case of “small” data, elaborate deep models can also work well for many computer vision tasks.

Shiguang Shan received Ph.D. degree in computer science from the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China, in 2004. He joined ICT, CAS in 2002 and became a Professor in 2010. He is now the Deputy Director of the Key Lab of Intelligent Information Processing of CAS. His research interests cover computer vision, pattern recognition, and machine learning, especially focusing on face recognition related research topics. His work on face recognition has been applied to many practical systems in China. He has published more than 150 papers in refereed journals and proceedings in the related areas, which has been cited more than 6,300 times in Google scholar. He has served as Area Chair for many international conferences including ICCV’11, ICPR’12, ACCV’12, FG’13, ICPR’14, and ICASSP’14. He is workshop co-chair of ACCV14, and website co-chair of ICCV15. He is Associate Editor of IEEE Trans. on Image Processing, Neurocomputing, and EURASIP Journal of Image and Video Processing.