Your browser does not support JavaScript!

Motion-based counter-measures to photo attacks in face recognition

Motion-based counter-measures to photo attacks in face recognition

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Identity spoofing is a contender for high-security face-recognition applications. With the advent of social media and globalised search, peoples face images and videos are wide-spread on the Internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy – the authors lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are 3-fold: first, the authors a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures is introduced. Based on the data available, a study is conducted on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of this new dataset. By last, the authors propose a new technique of counter-measure solely based on foreground/background motion correlation using optical flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this study.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 23. Liu, C. (ed.): ‘Beyond pixels: exploring new representations and applications for motion analysis’. Doctoral Thesis, Massachusetts Institute of Technology, May 2009.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 11. Tan, X., Li, Y., Liu, J., Jiang, L.: ‘Face liveness detection from a single image with sparse low rank bilinear discriminative model’, Comput. Vis. ECCV 2010, 2001, 6316, pp. 504517 (doi: 10.1007/978-3-642-15567-3_37).
    11. 11)
      • 24. Bruhn, A., Weickert, J., Schnörr, C.: ‘Lucas/kanade meets horn/schunck: combining local and global optic flow methods’, Int. J. Comput. Vis., 2005, 61, pp. 211231 (doi: 10.1023/B:VISI.0000045324.43199.43).
    12. 12)
      • 3. Rodrigues, R.N., Ling, L.L., Govindaraju, V.: ‘Robustness of multimodal biometric fusion methods against spoof attacks’, J. Vis. Lang. Comput., 2009, 20, (3), pp. 169179 (doi: 10.1016/j.jvlc.2009.01.010).
    13. 13)
      • 10. Bai, J., Ng, T., Gao, X., Shi, Y.: ‘Is physics-based liveness detection truly possible with a single image?’. Int. Symp. Circuits and Systems, 2010, pp. 34253428.
    14. 14)
      • 17. Kollreider, K., Fronthaler, H., Bigun, J.: ‘Non-intrusive liveness detection by face images’, Image Vis. Comput., 2009, 27, (3), pp. 233244 (doi: 10.1016/j.imavis.2007.05.004).
    15. 15)
      • 26. Ahonen, T., Hadid, A., Pietikäinen, M.: ‘Face recognition with local binary patterns’. European Conf. Computer Vision (ECCV), 2010, pp. 469481.
    16. 16)
      • 31. Chingovska, I., Anjos, A., Marcel, S.: ‘On the effectiveness of local binary patterns in face anti-spoofing’ (IEEE BioSIG, 2012).
    17. 17)
      • 18. Froba, B., Ernst, A.: ‘Face detection with the modified census transform’. IEEE Int. Conf. Automatic Face and Gesture Recognition, 2004, pp. 9196.
    18. 18)
      • 12. Määttä, J., Hadid, A., Pietikäinen, M.: ‘Face spoofing detection from single images using micro-texture analysis’. Int. Joint Conf. Biometrics (IJCB), 2011.
    19. 19)
      • 14. Pan, G., Sun, L., Wu, Z., Lao, S.: ‘Eyeblink-based anti-spoofing in face recognition from a generic web camera’. IEEE 11th Int. Conf. Computer Vision (2007), 2007, pp. 18.
    20. 20)
      • 22. Sun, D., Roth, S., Darmstadt, T., Black, M.J.: ‘Secrets of optical flow estimation and their principles’. IEEE Conf. Computer Vision and Pattern Recognition, CVPR.
    21. 21)
      • 19. Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: ‘The DET curve in assessment of detection task performance’. Fifth European Conf. Speech Communication and Technology, 1997, pp. 18951898.
    22. 22)
      • 28. Bishop, C.M.: ‘Pattern recognition and machine learning (Information Science and Statistics)’ (Springer, 2007, 1st edn.).
    23. 23)
      • 4. Galbally, J., McCool, C., Fierrez, J., Marcel, S., Ortega-Garcia, J.: ‘On the vulnerability of face verification systems to hill-climbing attacks’, Pattern Recognit., 2010, 43, (3), pp. 10271038 (doi: 10.1016/j.patcog.2009.08.022).
    24. 24)
      • 5. Jain, A.K., Flynn, P., Ross, A.A. (eds.): ‘Handbook of biometrics’ (Springer, 2008).
    25. 25)
      • 13. Anjos, A., Marcel, S.: ‘Counter-measures to photo attacks in face recognition: a public database and a baseline’. Int. Joint Conf. Biometrics (IJCB), 2011.
    26. 26)
      • 8. Toth, B.: ‘Biometric id card debates’. Newsletter Biometrie, 2005.
    27. 27)
      • 15. Pan, G., Sun, L., Wu, Z., Wang, Y.: ‘Monocular camera-based face liveness detection by combining eyeblink and scene context’, J. Telecommun. Syst., 2009, 47, (3–4), pp. 215225 (doi: 10.1007/s11235-010-9313-3).
    28. 28)
      • 6. Duc, N.M., Minh, B.Q.: ‘Your face is not your password’. Black Hat Conf., 2009.
    29. 29)
      • 1. Schuckers, S.A.C.: ‘Spoofing and anti-spoofing measures’, Inf. Secur. Tech. Rep., 2002, 7, pp. 5662 (doi: 10.1016/S1363-4127(02)00407-7).
    30. 30)
      • 27. Leslie, G., Farkas, M. (eds.): ‘Anthropometry of the head and face’ (Raven Press, 1994).
    31. 31)
      • 16. Bao, W., Li, H., Li, N., Jiang, W.: ‘A liveness detection method for face recognition based on optical flow field’. 2009 Int. Conf. Image Analysis and Signal Processing, 2009, pp. 233236.
    32. 32)
      • 25. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: ‘High accuracy optical flow estimation based on a theory for warping’. European Conf. Computer Vision (ECCV), 2004, pp. 2536.
    33. 33)
      • 2. Sun, T., Li, Q., Qiu, Z.: ‘Advances in biometric person authentication’ (Springer, 2005), Ch. A secure multimodal biomeric verification scheme, pp. 233240.
    34. 34)
      • 23. Liu, C. (ed.): ‘Beyond pixels: exploring new representations and applications for motion analysis’. Doctoral Thesis, Massachusetts Institute of Technology, May 2009.
    35. 35)
      • 21. Lucas, B.D., Kanade, T.: ‘An iterative image registration technique with an application to stereo vision’. Seventh Int. Joint Conf. Artificial Intelligence, 1981, pp. 674679.
    36. 36)
      • 30. Chakka, M.M., Anjos, A., Marcel, S., et al: ‘Competition on counter measures to 2-d facial spoofing attacks’. Int. Joint Conf. Biometrics (IJCB), 2011.
    37. 37)
      • 7. Thalheim, L., Krissler, J., Ziegler, P.-M.: ‘Body check: biometric access protection devices and their programs put to the test’, Magazin für Computer Technik (C'T), 2002, 11/02.
    38. 38)
      • 20. Horn, B.K.P., Schunck, B.G.: ‘Determining optical flow’, Artif. Intell., 1981, 17, pp. 185203 (doi: 10.1016/0004-3702(81)90024-2).
    39. 39)
      • 29. Riedmiller, M., Braun, H.: ‘A direct adaptive method for faster backpropagation learning: the rprop algorithm’. IEEE Int. Conf. Neural Networks, 1993, vol. 1, no. 3, pp. 586591.
    40. 40)
      • 9. Pan, G., Wu, Z., Sun, L.: ‘Liveness detection for face recognition’, in Delac, K., Grgic, M., Stewart Bartlett, M. (Eds.): ‘Recent advances in face recognition’, 2008, pp. 109124.

Related content

This is a required field
Please enter a valid email address