Fusing Multiple Deep Features for Face Anti-spoofing

  • Yan Tang
  • Xing Wang
  • Xi Jia
  • Linlin ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


With the growing deployment of face recognition system in recent years, face anti-spoofing has become increasingly important, due to the increasing number of spoofing attacks via printed photos or replayed videos. Motivated by the powerful representation ability of deep learning, in this paper we propose to use CNNs (Convolutional Neural Networks) to learn multiple deep features from different cues of the face images for anti-spoofing. We integrate temporal features, color based features and patch based local features for spoof detection. We evaluate our approach extensively on publicly available databases like CASIA FASD, REPLAY-MOBILE and OULU-NPU. The experimental results show that our approach can achieve much better performance than state-of-the-art methods. Specifically, 2.22% of EER (Equal Error Rate) on the CASIA FASD, 3.2% of ACER (Average Classification Error Rate) on the OULU-NPU (protocol 1) and 0.00% of ACER on the REPLAY-MOBILE database are achieved.


Deep convolutional neural networks Face anti-spoofing Multiple features 



The work is supported by Natural Science Foundation of China under grands No. 61672357 and U1713214.


  1. 1.
    Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)CrossRefGoogle Scholar
  2. 2.
    Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)CrossRefGoogle Scholar
  3. 3.
    Bengio, S., Mariéthoz, J.: A statistical significance test for person authentication. In: The Speaker and Language Recognition Workshop (Odyssey), pp. 237–244, Toledo (2004)Google Scholar
  4. 4.
    Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S. Z.: A Face antispoofing database with diverse attacks. In: IAPR International Conference on Biometrics, pp. 26–31 (2012)Google Scholar
  5. 5.
    Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 612–618 (2017)Google Scholar
  6. 6.
    Costa-Pazo, A., Bhattacharjee, S., Vazquez-Fernandez, E., Marcel, S.: The replay-mobile face presentation-attack database. In: Biometrics Special Interest Group (2016)Google Scholar
  7. 7.
    Boulkenafet, Z., Komulainen, J., Akhtar, Z., Benlamoudi, A., Samai, D., Bekhouche, S., et al.: A competition on generalized software-based face presentation attack detection in mobile scenarios. In: IEEE International Joint Conference on Biometrics (2017)Google Scholar
  8. 8.
    Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. Comput. Sci. 9218, 373–384 (2014)Google Scholar
  9. 9.
    Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face Anti-spoofing using patch and depth-based CNNs. In: IEEE International Joint Conference on Biometrics (2018)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.
    Wang, Y., Nian, F., Li, T., Meng, Z., Wang, K.: Robust face anti-spoofing with depth information. J. Vis. Commun. Image Represent. 49, 332–337 (2017)CrossRefGoogle Scholar
  12. 12.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on colour texture analysis. In: IEEE International Conference on Image Processing, pp. 2636–2640 (2015)Google Scholar
  13. 13.
    Feng, L., Po, L.M., Li, Y., Xu, X., Yuan, F., Cheung, C.H., et al.: Integration of image quality and motion cues for face anti-spoofing. J. Vis. Commun. Image Represent. 38(2), 451–460 (2016)CrossRefGoogle Scholar
  14. 14.
    Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  15. 15.
    Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: International Conference on Image Processing Theory TOOLS and Applications, pp. 1–6. IEEE (2017)Google Scholar
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  17. 17.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Sig. Process. Lett. 24, 141–145 (2017)Google Scholar
  18. 18.
    ISO/IEC JTC 1/SC 37 Biometrics. Information technology - Biometric Presentation attack detection - Part 1: Framework. International Organization for Standardization (2016)Google Scholar
  19. 19.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., et al.: Caffe: convolutional architecture for fast feature embedding. In: 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computer Vision Institute, School of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

Personalised recommendations