Shape Analysis Based Anti-spoofing 3D Face Recognition with Mask Attacks

  • Yinhang TangEmail author
  • Liming Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 684)


With the growth of face recognition, the spoofing mask attacks attract more attention in biometrics research area. In recent years, the countermeasures based on the texture and depth image against spoofing mask attacks have been reported, but the research based on 3D meshed sample has not been studied yet. In this paper, we propose to apply 3D shape analysis based on principal curvature measures to describe the meshed facial surface. Meanwhile, a verification protocol based on this feature descriptor is designed to verify person identity and to evaluate the anti-spoofing performance on Morpho database. Furthermore, for simulating a real-life testing scenario, FRGCv2 database is enrolled as an extension of face scans to augment the ratio of genuine face samples to fraud mask samples. The experimental results show that our system can guarantee a high verification rate for genuine faces and the satisfactory anti-spoofing performance against spoofing mask attacks in parallel.



This work was supported in part by the French research angency, l’Agence Nationale de Recherche (ANR), through the Biofence project under the grant ANR-13-INSE-0004-02.


  1. 1.
    Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition, vol. 101, pp. 1–15. Elsevier (2006)Google Scholar
  2. 2.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Expression-invariant 3D face recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 62–70. Springer, Heidelberg (2003). doi: 10.1007/3-540-44887-X_8 CrossRefGoogle Scholar
  3. 3.
    Chetty, G., Wagner, M.: Multi-level liveness verification for face-voice biometric authentication. In: 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, pp. 1–6. IEEE (2006)Google Scholar
  4. 4.
    Cohen-Steiner, D., Morvan, J.M.: Restricted delaunay triangulations and normal cycle. In: ACM, pp. 312–321 (2003)Google Scholar
  5. 5.
    De Marsico, M., Nappi, M., Riccio, D., Dugelay, J.L.: Moving face spoofing detection via 3D projective invariants. In: 5th IAPR International Conference on Biometrics, pp. 73–78. IEEE (2012)Google Scholar
  6. 6.
    Erdogmus, N., Marcel, S.: Spoofing in 2D face recognition with 3D masks and anti-spoofing with kinect. In: IEEE International Conference on BTAS, pp. 1–6 (2013)Google Scholar
  7. 7.
    Erdogmus, N., Marcel, S.: Spoofing face recognition with 3D masks. IEEE Trans. Inf. Forensics Secur. 9(7), 1084–1097 (2014)CrossRefGoogle Scholar
  8. 8.
    Gordon, G.G.: Face recognition based on depth and curvature features. In: IEEE Computer Society Conference on CVPR, pp. 808–810 (1992)Google Scholar
  9. 9.
    Huang, D., Ardabilian, M., Wang, Y., Chen, L.: 3-D face recognition using elbp-based facial description and local feature hybrid matching. IEEE Trans. Inf. Forensics Secur. 7(5), 1551–1565 (2012)CrossRefGoogle Scholar
  10. 10.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962)CrossRefGoogle Scholar
  11. 11.
    Jain, A., Hong, L., Pankanti, S.: Biometric identification. Commun. ACM 43(2), 90–98 (2000)CrossRefGoogle Scholar
  12. 12.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)CrossRefGoogle Scholar
  13. 13.
    Kim, Y., Na, J., Yoon, S., Yi, J.: Masked fake face detection using radiance measurements. JOSA A 26(4), 760–766 (2009)CrossRefGoogle Scholar
  14. 14.
    Kollreider, K., Fronthaler, H., Bigun, J.: Evaluating liveness by face images and the structure tensor. In: IEEE Workshop on Automatic Identification Advanced Technologies, pp. 75–80 (2005)Google Scholar
  15. 15.
    Kollreider, K., Fronthaler, H., Bigun, J.: Verifying liveness by multiple experts in face biometrics. In: IEEE Computer Society Conference on CVPRW, pp. 1–6 (2008)Google Scholar
  16. 16.
    Kose, N., Dugelay, J.L.: Countermeasure for the protection of face recognition systems against mask attacks. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013)Google Scholar
  17. 17.
    Kose, N., Dugelay, J.L.: On the vulnerability of face recognition systems to spoofing mask attacks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2357–2361. IEEE (2013)Google Scholar
  18. 18.
    Kose, N., Dugelay, J.L.: Shape and texture based countermeasure to protect face recognition systems against mask attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 111–116 (2013)Google Scholar
  19. 19.
    Li, H., Huang, D., Morvan, J.M., Wang, Y., Chen, L.: Towards 3D face recognition in the real: a registration-free approach using fine-grained matching of 3D keypoint descriptors. Int. J. Comput. Vision 113(2), 128–142 (2015)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Defense and Security, pp. 296–303 (2004)Google Scholar
  21. 21.
    Li, X., Jia, T., Zhang, H.: Expression-insensitive 3D face recognition using sparse representation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2575–2582 (2009)Google Scholar
  22. 22.
    Li, X., Zhang, H.: Adapting geometric attributes for expression-invariant 3D face recognition. In: IEEE International Conference on Shape Modeling and Applications, pp. 21–32 (2007)Google Scholar
  23. 23.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  24. 24.
    Lu, X., Jain, A.K., Colbry, D.: Matching 2.5D face scans to 3D models. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 31–43 (2006)CrossRefGoogle Scholar
  25. 25.
    Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: International Joint Conference on Biometrics, pp. 1–7. IEEE (2011)Google Scholar
  26. 26.
    Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2D–3D hybrid approach to automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1927–1943 (2007)CrossRefGoogle Scholar
  27. 27.
    Morvan, J.M.: Generalized Curvatures. Springer, Heidelberg (2008)CrossRefzbMATHGoogle Scholar
  28. 28.
    Nixon, K.A., Aimale, V., Rowe, R.K.: Spoof detection schemes. In: Jain, A.K., Flynn, P., Ross, A.A. (eds.) Handbook of Biometrics, pp. 403–423. Springer, New York (2008)CrossRefGoogle Scholar
  29. 29.
    Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: 11th IEEE International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  30. 30.
    Pears, N., Liu, Y., Bunting, P. (eds.): 3D Imaging, Analysis and Applications, vol. 3. Springer, London (2012)Google Scholar
  31. 31.
    Smeets, D., Keustermans, J., Vandermeulen, D., Suetens, P.: meshSIFT: local surface features for 3D face recognition under expression variations and partial data. Comput. Vis. Image Underst. 117(2), 158–169 (2013)CrossRefGoogle Scholar
  32. 32.
    Sun, X., Morvan, J.M.: Curvature measures, normal cycles and asymptotic cones. Actes des rencontres du C.I.R.M. 3(1), 3–10 (2013)CrossRefGoogle Scholar
  33. 33.
    Sun, X., Morvan, J.M.: Asymptotic cones of embedded singular spaces. arXiv preprint arXiv:1501.02639 (2015)
  34. 34.
    Szeptycki, P., Ardabilian, M., Chen, L.: A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In: 3rd IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2009)Google Scholar
  35. 35.
    Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based face surface recognition using spherical correlation. Principal directions for curved object recognition. In: 3rd IEEE International Conference on FG, pp. 372–377 (1998)Google Scholar
  36. 36.
    Tang, Y., Sun, X., Huang, D., Morvan, J.M., Wang, Y., Chen, L.: 3D face recognition with asymptotic cones based principal curvatures. In: IEEE International Conference on Biometrics, pp. 466–472 (2015)Google Scholar
  37. 37.
    Tola, E., Lepetit, V., Fua, P.: DAISY: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)CrossRefGoogle Scholar
  38. 38.
    Wu, Z., Wang, Y., Pan, G.: 3D face recognition using local shape map. In: IEEE International Conference on Image Processing, vol. 3, pp. 2003–2006 (2004)Google Scholar
  39. 39.
    Zhang, Z., Yi, D., Lei, Z., Li, S.Z.: Face liveness detection by learning multispectral reflectance distributions. In: IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, pp. 436–441 (2011)Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Université de Lyon, Ecole Centrale de Lyon, LIRIS laboratory UMR CNRS 5205LyonFrance

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