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A Discriminative Feature Learning Approach for Deep Face ...

A Discriminative Feature Learning Approachfor Deep Face RecognitionYandong Wen1, Kaipeng Zhang1, Zhifeng Li1(B), and Yu Qiao1,21 Shenzhen Key Lab of Computer Vision and Pattern Recognition,Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, Chinese University of Hong Kong, Sha Tin, Hong neural networks (CNNs) have been widelyused in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is usedas the supervision signal to train the deep model. In order to enhancethe Discriminative power of the deeply learned features, this paper pro-poses a new supervision signal, called center loss, for face recognitiontask. Specifically, the center loss simultaneously learns a center for deepfeatures of each class and penalizes the distances between the deep fea-tures and their corresponding class centers.

A Discriminative Feature Learning Approach for Deep Face Recognition 501 Inthispaper,weproposeanewlossfunction,namelycenterloss,toefficiently enhance the discriminative power of the deeply learned features in neural net-works. Specifically, we learn a center (a vector with the same dimension as a fea-ture) for deep features of each class.

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