Advertisement

Detection of Video-Based Face Spoofing Using LBP and Multiscale DCT

  • Ye Tian
  • Shijun XiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

Despite the great deal of progress during the recent years, face spoofing detection is still a focus of attention. In this paper, an effective, simple and time-saving countermeasure against video-based face spoofing attacks based on LBP (Local Binary Patterns) and multiscale DCT (Discrete Cosine Transform) is proposed. Adopted as the low-level descriptors, LBP features are used to extract spatial information in each selected frame. Next, multiscale DCT is performed along the ordinate axis of the obtained LBP features to extract spatial information. Representing both spatial and temporal information, the obtained high-level descriptors (LBP-MDCT features) are finally fed into a SVM (Support Vector Machine) classifier to determine whether the input video is a facial attack or valid access. Compared with state of the art, the excellent experimental results of the proposed method on two benchmarking datasets (Replay-Attack and CASIA-FASD dataset) have demonstrated its effectiveness.

Keywords

Video-based face spoofing LBP Multiscale DCT Replay-attack CASIA-FASD 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61272414) and the research funding of State Key Laboratory of Information Security (2016-MS-07).

References

  1. 1.
    Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access 2, 1530–1552 (2014)CrossRefGoogle Scholar
  2. 2.
    Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: 2011 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE Press, Washington, DC (2011)Google Scholar
  3. 3.
    Galbally, J., Fierrez, J., Alonso-Fernandez, F., Martinez-Diaz, M.: Evaluation of direct attacks to fingerprint verification systems. Telecommun. Syst. 47(3–4), 243–254 (2011)CrossRefGoogle Scholar
  4. 4.
    Mjaaland, B.B., Bours, P., Gligoroski, D.: Walk the walk: attacking gait biometrics by imitation. In: Burmester, M., Tsudik, G., Magliveras, S., Ilić, I. (eds.) ISC 2010. LNCS, vol. 6531, pp. 361–380. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-18178-8_31 CrossRefGoogle Scholar
  5. 5.
    Chen, H., Valizadegan, H., Jackson, C., Soltysiak, S., Jain, A.K.: Fake hands: spoofing hand geometry systems. In: 2005 Biometrics Consortium Conference (BCC) (2005)Google Scholar
  6. 6.
    Bin, Q., Jian-Fei, P., Guang-Zhong, C., Ge-Guo, D.: The anti-spoofing study of vein identification system. In: International Conference on Computational Intelligence and Security (ICCIS), pp. 357–360 (2009)Google Scholar
  7. 7.
    Akhtar, Z., Fumera, G., Marcialis, G.L., Roli, F.: Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 283–288 (2012)Google Scholar
  8. 8.
    Tome, P., Vanoni, M., Marcel, S.: On the vulnerability of finger vein recognition to spoofing. In: International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–10 (2014)Google Scholar
  9. 9.
    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). doi: 10.1007/978-3-642-15567-3_37 CrossRefGoogle Scholar
  10. 10.
    Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.: A face antispoofing database with diverse attacks. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 26–31. IEEE Press (2012)Google Scholar
  11. 11.
    Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1–7. IEEE Press, Darmstadt (2012)Google Scholar
  12. 12.
    Erdogmus, N., Marcel, S.: Spoofing in 2D face recognition with 3D masks and anti-spoofing with kinect. In: 6th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6 (2013)Google Scholar
  13. 13.
    Pinto, A., Schwartz, W.R., Pedrini, H., de Rezende Rocha, A.: Using visual rhythms for detecting video-based facial spoof attacks. IEEE Trans. Inf. Forensics Secur. 10(5), 1025–1038 (2015)CrossRefGoogle Scholar
  14. 14.
    Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. Proc. SPIE 5404, 296–303 (2004)CrossRefGoogle Scholar
  15. 15.
    Peixoto, B., Michelassi, C., Rocha, A.: Face liveness detection under bad illumination conditions. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3557–3560. IEEE Press, Brussels (2011)Google Scholar
  16. 16.
    Maatta, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1(1), 3–10 (2012)CrossRefGoogle Scholar
  17. 17.
    Kose, N., Dugelay, J.L.: Classification of captured and recaptured images to detect photograph spoofing. In: 2012 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, pp. 1027–1032 (2012)Google Scholar
  18. 18.
    Maatta, 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 Press, Washington, DC (2011)Google Scholar
  19. 19.
    Nguyen, H.H., Nguyen-Son, H.-Q., Nguyen, T.D., Echizen, I.: Discriminating between computer-generated facial images and natural ones using smoothness property and local entropy. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Echizen, I. (eds.) IWDW 2015. LNCS, vol. 9569, pp. 39–50. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-31960-5_4 CrossRefGoogle Scholar
  20. 20.
    de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBPTOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37410-4_11 CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Arashloo, S.R., Kittler, J., Christmas, W.: Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features. IEEE Trans. Inf. Forensics Secur. 10(11), 2396–2407 (2015)CrossRefGoogle Scholar
  23. 23.
    Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans. Image Process. 24(12), 4726–4740 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    da Silva Pinto, A., Pedrini, H., Schwartz, W., Rocha, A.: Video-based face spoofing detection through visual rhythm analysis. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, Ouro Preto, pp. 221–228 (2012)Google Scholar
  25. 25.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  26. 26.
    Arashloo, S.R., Kittler, J.: Class-specific kernel fusion of multiple descriptors for face verification using multiscale binarised statistical image features. IEEE Trans. Inf. Forensics Secur. 9(12), 2100–2109 (2014)CrossRefGoogle Scholar
  27. 27.
    Arashloo, S.R., Kittler, J.: Dynamic texture recognition using multiscale binarized statistical image features. IEEE Trans. Multimedia 16(8), 2099–2109 (2014)CrossRefGoogle Scholar
  28. 28.
    Chan, C.H., Tahir, M.A., Kittler, J., Pietikainen, M.: Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1164–1177 (2013)CrossRefGoogle Scholar
  29. 29.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  30. 30.
    de Freitas Pereira, T., Komulainen, J., Anjos, A., De Martino, J.M., Hadid, A., Pietikäinen, M., Marcel, S.: Face liveness detection using dynamic texture. EURASIP J. Image Video Process. 2014(2), 1–15 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Science and TechnologyJinan UniversityGuangzhouChina
  2. 2.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina

Personalised recommendations