From Unified Subspace Analysis to Unified Deep Learning: Ten Years Journey for Face Recognition

July 3, 2014
Location: 750 CEPSR
Speaker: Xiaogang Wang, Assistatant Professor, Department of Electronic Engineering, Chinese University of Hong Kong


In this talk, I will report on our most recent work on deep learning for face recognition. 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. This is the first time that a machine provided with only the face region achieves an accuracy on par with the 99.20% accuracy of a human to whom the entire LFW face image - including the face region and large background area - are presented for verification. Compared with the best previously achieved deep learning result (97.45%), the error rate has been significantly reduced by 67%.

Speaker Bio

Xiaogang Wang received his Bachelor's 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. He received his MPhil in Information Engineering from the Chinese University of Hong Kong in 2004 and his PhD in Computer Science from the Massachusetts Institute of Technology in 2009. He has been 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 Behavior Analysis Award in 2011, the Hong Kong RGC Early Career Award in 2012, and the 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.

Hosted by Shih-Fu Chang.

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