Ultra-deep Neural Network for Face Anti-spoofing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Face anti-spoofing is a hot research area in computer vision. With the progress of Deep Neural Networks (DNNs) in computer vision, some work has introduced neural networks into face anti-spoofing. However, the neural networks that most of the approaches use consist of only a few layers due to the limitation of training data. Inspired by the fact that deep efficiently trained neural networks are often possible to learn better representation than shallow networks. In this paper, we propose a fully data-driven ultra-deep model based on transfer learning. The model adopts a pre-trained deep residual network to learn highly discriminative features, and combines it with the Long Short-Term Memory (LSTM) units to discover long-range temporal relationships of from video frames for classification. We conduct extensive experiments on two most common benchmark datasets, namely, REPLAY-ATTACK and CASIA-FASD. Experimental results demonstrate that our ultra-deep network framework archives state-of-the-art performance.

Keywords

Face anti-spoofing Face liveness detection Deep learning Transfer learning 

Notes

Acknowledgements

The work is funded by the National Natural Science Foundation of China (No. 61371149, No. 61170155), Shanghai Innovation Action Plan Project (No. 16511101200) and the Open Project Program of the National Laboratory of Pattern Recognition (No. 201600017).

References

  1. 1.
    Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. CoRR abs/1507.06228 (2015)Google Scholar
  2. 2.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR abs/1409.4842 (2014)Google Scholar
  3. 3.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)Google Scholar
  4. 4.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)Google Scholar
  5. 5.
    Larsson, G., Maire, M., Shakhnarovich, G.: Fractalnet: ultra-deep neural networks without residuals. CoRR abs/1605.07648 (2016)Google Scholar
  6. 6.
    Chingovska, I., Yang, J., Lei, Z., Yi, D.: The 2nd competition on counter measures to 2d face spoofing attacks. In: International Conference on Biometrics, pp. 1–6 (2013)Google Scholar
  7. 7.
    Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access 2, 1530–1552 (2015)CrossRefGoogle Scholar
  8. 8.
    Komulainen, J., Hadid, A., Pietikainen, M.: Context based face anti-spoofing. In: IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems, pp. 1–8 (2013)Google Scholar
  9. 9.
    Diviya, M., Mishra, S.: A novel approach for detecting facial image spoofing using local ternary pattern. In: 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM), pp. 61–66. IEEE (2016)Google Scholar
  10. 10.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1818–1830 (2016)CrossRefGoogle Scholar
  11. 11.
    Li, J., Wang, Y., Jain, A.K.: Live face detection based on the analysis of Fourier spectra. Proc. SPIE 5404, 296–303 (2004)CrossRefGoogle Scholar
  12. 12.
    Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using micro-texture analysis. In: International Joint Conference on Biometrics, pp. 1–7 (2011)Google Scholar
  13. 13.
    Boulkenafet, Z., Komulainen, J., Feng, X., Hadid, A.: Scale space texture analysis for face anti-spoofing. In: International Conference on Biometrics, pp. 1–6 (2016)Google Scholar
  14. 14.
    Agarwal, A., Singh, R., Vatsa, M.: Face anti-spoofing using Haralick features. In: IEEE International Conference on Biometrics Theory, Applications and Systems (2016)Google Scholar
  15. 15.
    Pereira, T.D.F., Anjos, A., Martino, J.M.D., Marcel, S.: LBP - top based countermeasure against face spoofing attacks. In: International Conference on Computer Vision, pp. 121–132 (2012)Google Scholar
  16. 16.
    Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.T.S.: Detection of face spoofing using visual dynamics. IEEE Trans. Inf. Forensics Secur. 10(4), 762–777 (2015)CrossRefGoogle Scholar
  17. 17.
    Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. Comput. Sci. 9218, 373–384 (2014)Google Scholar
  18. 18.
    Yin, W., Ming, Y., Tian, L.: A face anti-spoofing method based on optical flow field. In: 2016 IEEE 13th International Conference on Signal Processing (ICSP), pp. 1333–1337. IEEE (2016)Google Scholar
  19. 19.
    Xu, Z., Li, S., Deng, W.: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: Pattern Recognition, pp. 141–145 (2016)Google Scholar
  20. 20.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735 (1997)CrossRefGoogle Scholar
  21. 21.
    Lin, M., Chen, Q., Yan, S.: Network in network. CoRR abs/1312.4400 (2013)Google Scholar
  22. 22.
    Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: Biometrics Special Interest Group, pp. 1–7 (2012)Google Scholar
  23. 23.
    Zhang, Z., Yan, J., Liu, S., Lei, Z.: A face antispoofing database with diverse attacks. In: IAPR International Conference on Biometrics, pp. 26–31 (2012)Google Scholar
  24. 24.
    Pan, G., Wu, Z., Sun, L.: Liveness Detection for Face Recognition. InTech (2008)Google Scholar
  25. 25.
    Anjos, A., Shafey, L.E., Wallace, R., Günther, M., McCool, C., Marcel, S.: Bob: a free signal processing and machine learning toolbox for researchers. In: 20th ACM Conference on Multimedia Systems (ACMMM), Nara, Japan. ACM Press, October 2012Google Scholar
  26. 26.
    Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6, December 2016Google Scholar
  27. 27.
    Alotaibi, A., Mahmood, A.: Deep face liveness detection based on nonlinear diffusion using convolution neural network. Signal Image Video Process. 11(4), 1–8 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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