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Deep Learning for Face Recognition
Date:2014/12/3     Browse:1954

Speaker: Xiaogang Wang

Time: Dec. 3, 3:15-4:45 pm

Location: 15th Floor Lecture Hall, Building 8, Yueyang Road Campus

Abstract:

In this talk, I will report our most recent works on deep learning for face recognition, i.e. DeepID2 and Multi-View Perception (MVP). With a novel deep model and a moderate training set with 200,000 face images, 99.15% accuracy has been achieved on LFW, the most challenging and extensively studied face recognition dataset. Deep learning provides a powerful tool to separate intra-personal and inter-personal variations, whose distributions are complex and highly nonlinear, through hierarchical feature transforms. It is essential to learn effective face representations by using two supervisory signals simultaneously, i.e. the face identification and verification signals. Some people understand the success of deep learning as using a complex model with many parameters to fit a dataset. To clarify such misunderstanding, we further investigate face recognition process in deep nets, what information is encoded in neurons, and how robust they are to data corruptions. We discovered several interesting properties of deep nets, including sparseness, selectiveness and robustness.

In Multi-View Perception, a hybrid deep model is proposed to simultaneously accomplish the tasks of face recognition, pose estimation, and face reconstruction. It employs deterministic and random neurons to encode identity and pose information respectively. Given a face image taken in an arbitrary view, it can untangle the identity and view features, and in the meanwhile the full spectrum of multi-view images of the same identity can be reconstructed. It is also capable to interpolate and predict images under viewpoints that are unobserved in the training data.

Bio:

Xiaogang Wang received his Bachelor degree in Electrical Engineering and Information Science from the Special Class of Gifted Young at the University of Science and Technology of China in 2001, M. Phil. degree in Information Engineering from the Chinese University of Hong Kong in 2004, and PhD degree in Computer Science from Massachusetts Institute of Technology in 2009. He is an assistant professor in the Department of Electronic Engineering at the Chinese University of Hong Kong since August 2009. He received the Outstanding Young Researcher in Automatic Human Behaviour Analysis Award in 2011, Hong Kong RGC Early Career Award in 2012, and Young Researcher Award of the Chinese University of Hong Kong. He is the associate editor of the Image and Visual Computing Journal. He was the area chair of ICCV 2011, ECCV 2014 and ACCV 2014. His research interests include computer vision, deep learning, crowd video surveillance, object detection, and face recognition.

                                                                                                SIST-Seminar 14042