Face Anti-spoofing Based on Specular Feature Projections

  • Balaji Rao KatikaEmail author
  • Kannan Karthik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)


The need for facial anti-spoofing has emerged to counter the usage of facial prosthetics and other forms of spoofing at unmanned surveillance stations. While some part of literature recognizes the difference in texture associated with a prosthetic in comparison with a genuine face, the solutions presented are largely prosthetic model specific and rely on two-sided calibration and training. In this paper, we focus on the specular component associated with genuine faces and claim that on account of the natural depth variation, its feature diversity is expected to be much larger as compared to prosthetics or even printed photo impersonations. In our work concerning one-sided calibration, we first characterize the specular feature space corresponding to genuine images and learn the projections of genuine and spoof data onto this basis. The trained SVM corresponding to genuine projections, 3D mask projections, and printed photo projections is then used as an anti-spoofing model for detecting impersonations.


Face Anti-spoofing Specular feature Eigenspace Printed photos 3D Mask Low-rank component Sparse component SVM classifier 


  1. 1.
    Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Erdogmus, N., Marcel, S.: Spoofing face recognition with 3d masks. IEEE Trans. Inf. Forensics Secur. 9(7), 1084–1097 (2014)CrossRefGoogle Scholar
  3. 3.
    Galbally, J., Marcel, S.: Face anti-spoofing based on general image quality assessment. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 1173–1178. IEEE (2014)Google Scholar
  4. 4.
    Gao, X., Ng, T.T., Qiu, B., Chang, S.F.: Single-view recaptured image detection based on physics-based features. In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp. 1469–1474. IEEE (2010)Google Scholar
  5. 5.
    Garcia, D.C., de Queiroz, R.L.: Face-spoofing 2d-detection based on moiré-pattern analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 778–786 (2015)CrossRefGoogle Scholar
  6. 6.
    Ji, Z., Zhu, H., Wang, Q.: Lfhog: a discriminative descriptor for live face detection from light field image. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1474–1478. IEEE (2016)Google Scholar
  7. 7.
    Karthik, K., Katika, B.R.: Face anti-spoofing based on sharpness profiles. In: 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), pp. 1–6. IEEE (2017)Google Scholar
  8. 8.
    Karthik, K., Katika, B.R.: Image quality assessment based outlier detection for face anti-spoofing. In: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 72–77. IEEE (2017)Google Scholar
  9. 9.
    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
  10. 10.
    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

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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