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Face Recognition with Contrastive Convolution

  • Chunrui Han
  • Shiguang Shan
  • Meina Kan
  • Shuzhe Wu
  • Xilin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

In current face recognition approaches with convolutional neural network (CNN), a pair of faces to compare are independently fed into the CNN for feature extraction. For both faces the same kernels are applied and hence the representation of a face stays fixed regardless of whom it is compared with. As for us humans, however, one generally focuses on varied characteristics of a face when comparing it with distinct persons as shown in Fig. 1. Inspired, we propose a novel CNN structure with what we referred to as contrastive convolution, which specifically focuses on the distinct characteristics between the two faces to compare, i.e., those contrastive characteristics. Extensive experiments on the challenging LFW, and IJB-A show that our proposed contrastive convolution significantly improves the vanilla CNN and achieves quite promising performance in face verification task.

Keywords

Face recognition Convolutional neural networks Contrastive convolution Kernel generator 

Notes

Acknowledgement

This work was partially supported by National Key Research and Development Program of China Grant 2017YFA0700804, Natural Science Foundation of China under contracts Nos. 61390511, 61650202, and 61772496.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of SciencesInstitute of Computing Technology, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

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