Advertisement

A Discriminative Feature Learning Based on Deep Residual Network for Face Verification

  • Tong Zhang
  • Rong Wang
  • Jianwei Ding
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

The face verification system based on deep convolutional neural networks (DCNNs) has achieved great success. The architecture of existing methods is somehow shallow because of the insufficient convolutional layers. And the softmax loss function does not enlarge inter-class variations and minimize the intra-class variations. In this paper, a residual network was adopted as the core architecture to extract the discriminative features and it was trained with joint supervision of center loss and softmax loss. The public available CASIA-Webface dataset was used as the training data to train our model for face verification, and the model was tested on LFW and CAS-PEAL-R1 datasets. Experimental results show that our method achieves higher accuracy on LFW and has better robustness than the shallow model such as VGG Face.

Keywords

Convolutional neural network Face verification Residual network Center loss 

Notes

Acknowledgments

This work is supported by National Key Research and Development Plan under Grant No. 2016YFC0801005 and the National Nature Science Foundation of China (Grant No. 61503388).

References

  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  2. 2.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  3. 3.
    Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Press (2014)Google Scholar
  4. 4.
    Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE Press (2016)Google Scholar
  5. 5.
    He, K., Gkioxari, G., Dollár, P., et al.: Mask R-CNN. arXiv Preprint arXiv:170306870 (2017)
  6. 6.
    Caelles, S., Maninis, K.K., Pont-Tuset, J., et al.: One-Shot Video Object Segmentation (2016)Google Scholar
  7. 7.
    Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRefGoogle Scholar
  8. 8.
    Huang, G.B., Ramesh, M., Berg, T., et al.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts, Amherst (2007)Google Scholar
  9. 9.
    Taigman, Y., Yang, M., Ranzato, M.A., et al.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE Press (2014)Google Scholar
  10. 10.
    Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE Press (2014)Google Scholar
  11. 11.
    Sun, Y., Chen, Y., Wang, X., et al.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)Google Scholar
  12. 12.
    Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900. IEEE Press (2015)Google Scholar
  13. 13.
    Sun, Y., Liang, D., Wang, X., et al.: DeepID3: face recognition with very deep neural networks. arXiv Preprint arXiv:150200873 (2015)
  14. 14.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823. IEEE Press (2015)Google Scholar
  15. 15.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. arXiv:13112901 (2013)
  16. 16.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE Press (2015)Google Scholar
  17. 17.
    Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)Google Scholar
  18. 18.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:14091556 (2014)
  19. 19.
    Liu, W., Wen, Y., Yu, Z., et al.: SphereFace: deep hypersphere embedding for face recognition. arXiv Preprint arXiv:170408063 (2017)
  20. 20.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46478-7_31CrossRefGoogle Scholar
  21. 21.
    Zhang, K., Zhang, Z., Li, Z., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)CrossRefGoogle Scholar
  22. 22.
    Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv Preprint arXiv:14117923 (2014)
  23. 23.
    Deng, J., Dong, W., Socher, R., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 248–255. IEEE Press (2009)Google Scholar
  24. 24.
    Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  25. 25.
    Abadi, M., Barham, P., Chen, J., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv Preprint arXiv:160304467 (2016)

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.People’s Public Security University of ChinaBeijingChina

Personalised recommendations