A Discriminative Feature Learning Approach for Deep Face Recognition
Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In o
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Shenzhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, China [email protected], {kp.zhang,zhifeng.li,yu.qiao}@siat.ac.cn 2 The Chinese University of Hong Kong, Sha Tin, Hong Kong
Abstract. Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-ofthe-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks. Keywords: Convolutional neural networks · Face recognition · Discriminative feature learning · Center loss
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Introduction
Convolutional neural networks (CNNs) have achieved great success on vision community, significantly improving the state of the art in classification problems, such as object [11,12,18,28,33], scene [41,42], action [3,16,36] and so on. It mainly benefits from the large scale training data [8,26] and the end-to-end learning framework. The most commonly used CNNs perform feature learning c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VII, LNCS 9911, pp. 499–515, 2016. DOI: 10.1007/978-3-319-46478-7 31
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Input
Convolutional Feature Learning
Deeply learned Features
Predicted Labels
Label Prediction
Loss Function
Classify
Face Images
Separable Features
Discriminative Features
Predicted Labels
Fig. 1. The typical framework of convolutional neural networks.
and label prediction, mapping the input data to deep features (the output of the last hidden layer), then to the predicted labels, as shown in Fig. 1. In generic object, scene or action recognition, the classes of the possible testing samples are within the training set, which is also referred
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