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3D Mask Face Anti-spoofing with Remote Photoplethysmography

  • Siqi Liu
  • Pong C. YuenEmail author
  • Shengping Zhang
  • Guoying Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9911)

Abstract

3D mask spoofing attack has been one of the main challenges in face recognition. Among existing methods, texture-based approaches show powerful abilities and achieve encouraging results on 3D mask face anti-spoofing. However, these approaches may not be robust enough in application scenarios and could fail to detect imposters with hyper-real masks. In this paper, we propose a novel approach to 3D mask face anti-spoofing from a new perspective, by analysing heartbeat signal through remote Photoplethysmography (rPPG). We develop a novel local rPPG correlation model to extract discriminative local heartbeat signal patterns so that an imposter can better be detected regardless of the material and quality of the mask. To further exploit the characteristic of rPPG distribution on real faces, we learn a confidence map through heartbeat signal strength to weight local rPPG correlation pattern for classification. Experiments on both public and self-collected datasets validate that the proposed method achieves promising results under intra and cross dataset scenario.

Keywords

Face anti-spoofing 3D mask attack Remote photoplethysmography 

Notes

Acknowledgement

We thank Baoyao Yang for her help on drawing Fig. 1. This project is partially supported by Hong Kong RGC General Research Fund HKBU 12201215, Academy of Finland and FiDiPro program of Tekes (project number: 1849/31/2015).

References

  1. 1.
    Rattani, A., Poh, N., Ross, A.: Analysis of user-specific score characteristics for spoof biometric attacks. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 124–129. IEEE (2012)Google Scholar
  2. 2.
    Evans, N., Kinnunen, T., Yamagishi, J.: Spoofing and countermeasures for automatic speaker verification. In: INTERSPEECH, pp. 925–929 (2013)Google Scholar
  3. 3.
    Pavlidis, I., Symosek, P.: The imaging issue in an automatic face/disguise detection system. In: Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, pp, 15–24. IEEE (2000)Google Scholar
  4. 4.
    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
  5. 5.
    Määttä, J., Hadid, A., Pietikainen, 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
  6. 6.
    Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: 2011 international joint conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)Google Scholar
  7. 7.
    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
  8. 8.
    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, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  9. 9.
    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(1), 1–15 (2014)CrossRefGoogle Scholar
  10. 10.
    Kose, N., Dugelay, J.L.: Mask spoofing in face recognition and countermeasures. Image Vis. Comput. 32(10), 779–789 (2014)CrossRefGoogle Scholar
  11. 11.
    Yi, D., Lei, Z., Zhang, Z., Li, S.Z.: Face anti-spoofing: multi-spectral approach. In: Marcel, S., Nixon, M.S., Li, S.Z. (eds.) Handbook of Biometric Anti-Spoofing, pp. 83–102. Springer, London (2014)Google Scholar
  12. 12.
    Kose, N., Dugelay, J.L.: Shape and texture based countermeasure to protect face recognition systems against mask attacks. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 111–116. IEEE (2013)Google Scholar
  13. 13.
    Erdogmus, N., Marcel, S.: Spoofing face recognition with 3D masks. IEEE Trans. Inf. Forensics Secur. 9(7), 1084–1097 (2014)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434 (2008)CrossRefGoogle Scholar
  18. 18.
    de Haan, G., Jeanne, V.: Robust pulse rate from chrominance-based rPPG. IEEE Trans. Bio-Med. Eng. 60(10), 2878 (2013)CrossRefGoogle Scholar
  19. 19.
    Li, X., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situations. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4264–4271, June 2014Google Scholar
  20. 20.
    Lempe, G., Zaunseder, S., Wirthgen, T., Zipser, S., Malberg, H.: ROI selection for remote photoplethysmography. In: Meinzer, H.-P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2013, pp. 99–103. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Menotti, D., Chiachia, G., Pinto, A., Schwartz, W.R., Pedrini, H., Falcao, A., Rocha, A.X.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)CrossRefGoogle Scholar
  22. 22.
    Kollreider, K., Fronthaler, H., Faraj, M.I., Bigun, J.: Real-time face detection and motion analysis with application in liveness assessment. IEEE Trans. Inf. Forensics Secur. 2(3), 548–558 (2007)CrossRefGoogle Scholar
  23. 23.
    Wang, T., Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection using 3D structure recovered from a single camera. In: 2013 International Conference on Biometrics (ICB), pp. 1–6. IEEE (2013)Google Scholar
  24. 24.
    Komulainen, J., Hadid, A., Pietikainen, M.: 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
  25. 25.
    Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762–10774 (2010)CrossRefGoogle Scholar
  26. 26.
    Lewandowska, M., Ruminski, J., Kocejko, T., Nowak, J.: Measuring pulse rate with a webcama non-contact method for evaluating cardiac activity. In: 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 405–410. IEEE (2011)Google Scholar
  27. 27.
    Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J.F., Sebe, N.: Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016Google Scholar
  28. 28.
    Kumar, M., Veeraraghavan, A., Sabharwal, A.: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6(5), 1565 (2015)CrossRefGoogle Scholar
  29. 29.
    Cox, M., Nuevo-Chiquero, J., Saragih, J., Lucey, S.: CSIRO face analysis SDK, Brisbane, Australia (2013)Google Scholar
  30. 30.
    Wang, W., Stuijk, S., de Haan, G.: Exploiting spatial redundancy of image sensor for motion robust rPPG. IEEE Trans. Biomed. Eng. 62(2), 415–425 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Siqi Liu
    • 1
  • Pong C. Yuen
    • 1
    Email author
  • Shengping Zhang
    • 2
  • Guoying Zhao
    • 3
  1. 1.Department of Computer ScienceHong Kong Baptist UniversityKowloon TongHong Kong
  2. 2.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  3. 3.Center for Machine Vision and Signal AnalysisUniversity of OuluOuluFinland

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