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Real-Time Face Antispoofing Using Shearlets

  • Dustin Terence van der HaarEmail author
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 973)

Abstract

Face recognition. A promise made to the modern technologists as the ultimate access control or surveillance technology. However, it is still vulnerable to inexpensive spoofing attacks, which pose a threat to security. Basic face spoofing attacks that use photographs and video are still not addressed appropriately, especially in real-time applications, thereby making security in these environments a difficult task to achieve. Although methods have improved over the last decade, a robust solution that can accommodate changing environments is still out of reach. Face spoofing attacks introduce an object into the scene, which presents curvilinear singularities that are not necessarily portrayed in the same way in different lighting conditions. We present a solution that addresses this problem by using a discrete shearlet transform as an alternative descriptor that can differentiate between a real and a fake face without user-cooperation. We have found the approach can successfully detect blurred edges, texture changes and other noise found in various face spoof attacks. Our benchmarks on the publicly available CASIA-FASD, MSU-MFSD, and OULU-NPU data sets, show that our approach portrays good results and improves on the most popular methods found in the field on modest computer hardware, but requires further improvement to beat the current state of the art. The approach also achieves real-time face spoof discrimination, which makes it a practical solution in real-time applications and a viable augmentation to current face recognition methods.

Keywords

Face recognition Face antispoofing Presentation attack detection 
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References

  1. 1.
    Agarwal, A., Singh, R., Vatsa, M.: Face anti-spoofing using haralick features. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–6. IEEE (2016)Google Scholar
  2. 2.
    Bartlett, M.S., Littlewort, G., Fasel, I., Movellan, J.R.: Real time face detection and facial expression recognition: development and applications to human computer interaction. In: 2003 Conference on Computer Vision and Pattern Recognition Workshop, vol. 5, pp. 53–53, June 2003Google Scholar
  3. 3.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2636–2640. IEEE (2015)Google Scholar
  4. 4.
    Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 612–618. IEEE (2017)Google Scholar
  5. 5.
    Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG-Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–7. IEEE (2012)Google Scholar
  6. 6.
    de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBP-TOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37410-4_11CrossRefGoogle Scholar
  7. 7.
    de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: Can face anti-spoofing countermeasures work in a real world scenario? In: 2013 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2013)Google Scholar
  8. 8.
    de Freitas Pereira, T., et al.: Face liveness detection using dynamic texture. EURASIP J. Image Video Process. 2014(1), 2 (2014)CrossRefGoogle Scholar
  9. 9.
    Feng, L., et al.: Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J. Vis. Commun. Image Represent. 38, 451–460 (2016)CrossRefGoogle Scholar
  10. 10.
    Galbally, J., Marcel, S.: Face anti-spoofing based on general image quality assessment. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 1173–1178. IEEE (2014)Google Scholar
  11. 11.
    Komulainen, J., Hadid, A., Pietikainen, A.: Context based face anti-spoofing. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2013)Google Scholar
  12. 12.
    Kutyniok, G., Petersen, P.: Classification of edges using compactly supported shearlets. Appl. Comput. Harmon. Anal. 42(2), 245–293 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Li, Y., Po, L.-M., Xuyuan, X., Feng, L.: No-reference image quality assessment using statistical characterization in the shearlet domain. Signal Process. Image Commun. 29(7), 748–759 (2014)CrossRefGoogle Scholar
  14. 14.
    Lim, W.-Q.: The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans. Image Process. 19(5), 1166–1180 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Liu, X., Shan, S., Chen, X.: Face recognition after plastic surgery: a comprehensive study. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7725, pp. 565–576. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37444-9_44CrossRefGoogle Scholar
  16. 16.
    Lucena, O., Junior, A., Moia, V., Souza, R., Valle, E., Lotufo, R.: Transfer learning using convolutional neural networks for face anti-spoofing. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 27–34. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59876-5_4CrossRefGoogle Scholar
  17. 17.
    Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 international joint conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)Google Scholar
  18. 18.
    Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: IEEE 11th International Conference on Computer Vision, 2007, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  19. 19.
    Patel, K., Han, H., Jain, A.K., Ott, G.: Live face video vs. spoof face video: use of moiré patterns to detect replay video attacks. In: 2015 International Conference on Biometrics (ICB), pp. 98–105. IEEE (2015)Google Scholar
  20. 20.
    Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 504–517. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15567-3_37CrossRefGoogle Scholar
  21. 21.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  22. 22.
    Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensic Secur. 10(4), 746–761 (2015)CrossRefGoogle Scholar
  23. 23.
    Xu, Z., Li, S., Deng, W.: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 141–145, November 2015Google Scholar
  24. 24.
    Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection with component dependent descriptor. In: 2013 International Conference on Biometrics (ICB), pp. 1–6, June 2013Google Scholar
  25. 25.
    Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: 2012 5th IAPR international conference on Biometrics (ICB), pp. 26–31. IEEE (2012)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Academy of Computer Science and Software EngineeringUniversity of JohannesburgJohannesburgSouth Africa

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