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Deep Learning Face Representation by Joint Identification-Verification
Yi Sun · Yuheng Chen · Xiaogang Wang · Xiaoou Tang

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 features extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 features extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset, 99.15% face verification accuracy is achieved. Compared with the best previous deep learning result on LFW, the error rate has been significantly reduced by 67%.

Author Information

Yi Sun (The Chinese University of Hong Kong)
Yuheng Chen (Tsinghua University)
Xiaogang Wang (The Chinese University of Hong Kong)
Xiaoou Tang (Chinese University of Hong Kong)

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