A Discriminative Feature Learning Approach for Deep Face Recognition

Conference paper

DOI: 10.1007/978-3-319-46478-7_31

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)
Cite this paper as:
Wen Y., Zhang K., Li Z., Qiao Y. (2016) A Discriminative Feature Learning Approach for Deep Face Recognition. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9911. Springer, Cham


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 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.


Convolutional neural networks Face recognition Discriminative feature learning Center loss 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yandong Wen
    • 1
  • Kaipeng Zhang
    • 1
  • Zhifeng Li
    • 1
  • Yu Qiao
    • 1
    • 2
  1. 1.Shenzhen Key Lab of Computer Vision and Pattern RecognitionShenzhen Institutes of Advanced Technology, CASShenzhenChina
  2. 2.The Chinese University of Hong KongSha TinHong Kong

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