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Face spoofing detection based on colour distortions

Face spoofing detection based on colour distortions

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Securing face recognition systems against spoofing attacks have been recognised as a real challenge. Spoofing attacks are conducted by printing or displaying a digital acquisition of a capture subject (target user) in front of the sensor. These extra reproduction stages generate colour distortions between face artefacts and real faces. In this work, the problem of spoof detection is addressed by modelling the radiometric distortions generated by the recapturing process. The spoof detection process takes advantage of enrolment data and occurs after face identification so that for each client the authors have at disposal at least one genuine face sample as a reference. Once identified, they compute the colour transformation between the observed face and its enrolment counterpart. A compact parametric representation is proposed to model those radiometric transforms and it is used as features for classification. They evaluate the proposed method on Replay-Attack, CASIA and MSU public databases and show its competitiveness with state-of-the-art countermeasures. Limitations of the proposed method are clearly identified and discussed through experiments in adversary evaluation conditions where colour distortions are not only generated by the recapturing process but also by natural illumination variations.

References

    1. 1)
      • 1. VeriFace. http://en.wikipedia.org/wiki/VeriFace.
    2. 2)
      • 2. FaceUnlock. http://www.android.com/about/ice-cream-sandwich.
    3. 3)
      • 3. FaceLock Pro. http://www.facelock.mobi/facelock-for-apps.
    4. 4)
      • 4. Duc, N.M., Minh, B.Q.: ‘Your face is NOT your password face authentication ByPassing Lenovo–Asus–Toshiba’. Black Hat Briefings, 2009.
    5. 5)
      • 5. Li, Y., Li, Y., Xu, K., et al : ‘Empirical study of face authentication systems under OSNFD attacks’, IEEE Trans. Dependable Secur. Comput., 2016 , pp. 11.
    6. 6)
      • 6. Galbally, J., Marcel, S. : ‘Face anti-spoofing based on general image quality assessment’, Pattern Recognition (ICPR), 2014 22nd International Conference on Pattern Recognition, 2014 , pp. 11731178.
    7. 7)
      • 7. Caetano Garcia, D., de Queiroz, R.L.: ‘Face-spoofing 2D-detection based on moiré-pattern analysis’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 778786.
    8. 8)
      • 8. Patel, K., Han, H., Jain, A.K., et al: ‘Live face video vs. spoof face video: use of moiré patterns to detect replay video attacks’. 2015 Int. Conf. Biometrics (ICB), 2015, pp. 98105.
    9. 9)
      • 9. Pinto, A., Pedrini, H., Schwartz, W., et al: ‘Face spoofing detection through visual codebooks of spectral temporal cubes’, IEEE Trans. Image Process., 2015, PP, (99), pp. 11.
    10. 10)
      • 10. Pinto, A., Robson Schwartz, W., Pedrini, H., et al: ‘Using visual rhythms for detecting video-based facial spoof attacks’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (5), pp. 10251038.
    11. 11)
      • 11. Kose, N., Dugelay, J.-L.: ‘Reflectance analysis based countermeasure technique to detect face mask attacks’. 2013 18th Int. Conf. Digital Signal Processing (DSP), 2013, pp. 16.
    12. 12)
      • 12. Li, J., Wang, Y., Tan, T., et al: ‘Live face detection based on the analysis of Fourier spectra’. Defense and Security, 2004, pp. 296303.
    13. 13)
      • 13. Peixoto, B., Michelassi, C., Rocha, A.: ‘Face liveness detection under bad illumination conditions’. 2011 18th IEEE Int. Conf. Image Processing (ICIP), 2011, pp. 35573560.
    14. 14)
      • 14. Tan, X., Li, Y., Liu, J., et al: ‘Face liveness detection from a single image with sparse low rank bilinear discriminative model’. Computer Vision–ECCV 2010, 2010, pp. 504517.
    15. 15)
      • 15. Chingovska, I., Anjos, A., Marcel, S.: ‘On the effectiveness of local binary patterns in face anti-spoofing’. 2012 BIOSIG-Proc. of the Int. Conf. Biometrics Special Interest Group (BIOSIG), 2012, pp. 17.
    16. 16)
      • 16. Pereira, T.F., Anjos, A., De Martino, J.M., et al: ‘Can face anti-spoofing countermeasures work in a real world scenario?’. Int. Conf. Biometrics, ICB 2013, 2013.
    17. 17)
      • 17. Kose, N., Dugelay, J.-L.: ‘Countermeasure for the protection of face recognition systems against mask attacks’. FG 2013, 10th IEEE Int. Conf. Automatic Face and Gesture Recognition, Shanghai, China, 22–26 April 2013.
    18. 18)
      • 18. Maatta, J., Hadid, A., Pietikainen, M.: ‘Face spoofing detection from single images using micro-texture analysis’. 2011 Int. Joint Conf. Biometrics (IJCB), 2011, pp. 17.
    19. 19)
      • 19. Schwartz, W., Rocha, A., Pedrini, H.: ‘Face spoofing detection through partial least squares and low-level descriptors’. 2011 Int. Joint Conf. Biometrics (IJCB), 2011, pp. 18.
    20. 20)
      • 20. Waris, M.-A., Zhang, H., Ahmad, I., et al: ‘Analysis of textural features for face biometric anti-spoofing’. 21st European Signal Processing Conf. (EUSIPCO 2013), 2013, pp. 15.
    21. 21)
      • 21. Yang, J., Lei, Z., Liao, S., et al: ‘Face liveness detection with component dependent descriptor’. 2013 Int. Conf. Biometrics (ICB), 2013.
    22. 22)
      • 22. Boulkenafet, Z., Komulainen, J., Hadid, A.: ‘Face spoofing detection using colour texture analysis’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (8), pp. 18181830.
    23. 23)
      • 23. Wen, D., Han, H., Jain, A.K.: ‘Face spoof detection with image distortion analysis’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 746761.
    24. 24)
      • 24. Edmunds, T., Caplier, A.: ‘Fake face detection based on radiometric distortions’. 2016 6th Int. Conf. Image Processing Theory Tools and Applications (IPTA), 2016, pp. 16.
    25. 25)
      • 25. Galbally, J., Marcel, S., Fierrez, J.: ‘Biometric antispoofing methods: a survey in face recognition’, IEEE Access, 2014, 2, pp. 15301552.
    26. 26)
      • 26. Pan, G., Sun, L., Wu, Z., et al: ‘Monocular camera-based face liveness detection by combining eyeblink and scene context’, Telecommun. Syst., 2010, 47, (3–4), pp. 215225.
    27. 27)
      • 27. Komulainen, J., Hadid, A., Pietikainen, M.: ‘Context based face anti-spoofing’. 2013 IEEE Sixth Int. Conf. Biometrics: Theory, Applications and Systems (BTAS), 2013, pp. 18.
    28. 28)
      • 28. Pan, G., Sun, L., Wu, Z., et al: ‘Eyeblink-based anti-spoofing in face recognition from a generic webcamera’. IEEE 11th Int. Conf. Computer Vision, 2007, ICCV 2007, 2007, pp. 18.
    29. 29)
      • 29. Kollreider, K., Fronthaler, H., Faraj, M.I., et al: ‘Real-time face detection and motion analysis with application in liveness assessment’, IEEE Trans. Inf. Forensics Sec., 2007, 2, (3), pp. 548558.
    30. 30)
      • 30. Yan, J., Zhang, Z., Lei, Z., et al: ‘Face liveness detection by exploring multiple scenic clues’. 2012 12th Int. Conf. Control Automation Robotics & Vision (ICARCV), 2012, pp. 188193.
    31. 31)
      • 31. Chaudhry, R., Ravichandran, A., Hager, G., et al: ‘Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions’. IEEE Conf. Computer Vision and Pattern Recognition, 2009. CVPR 2009, 2009, pp. 19321939.
    32. 32)
      • 32. Chingovska, I.: ‘The 2nd competition on counter measures to 2D face spoofing attacks’. 2013 Int. Conf. Biometrics (ICB), 2013.
    33. 33)
      • 33. Kollreider, K., Fronthaler, H., Bigun, J.: ‘Non-intrusive liveness detection by face images’, Image Vis. Comput., 2007, 27, (3), pp. 233244.
    34. 34)
      • 34. De Marsico, M., Nappi, M., Riccio, D., et al: ‘Moving face spoofing detection via 3D projective invariants’. 2012 5th IAPR Int. Conf. Biometrics (ICB), 2012, pp. 7378.
    35. 35)
      • 35. Wang, T., Yang, J., Lei, Z., et al: ‘Face liveness detection using 3D structure recovered from a single camera’, 2013.
    36. 36)
      • 36. Anjos, A., Marcel, S.: ‘Counter-measures to photo attacks in face recognition: a public database and a baseline’. 2011 Int. Joint Conf. Biometrics (IJCB), 2011, pp. 17.
    37. 37)
      • 37. Anjos, A., Chakka, M.M., Marcel, S.: ‘Motion-based counter-measures to photo attacks in face recognition’, IET Biometrics, 2014, 3, (3), pp. 147158.
    38. 38)
      • 38. Chakka, M.M., Anjos, A., Marcel, S., et al: ‘Competition on counter measures to 2-d facial spoofing attacks’. 2011 Int. Joint Conf. Biometrics (IJCB), 2011, pp. 16.
    39. 39)
      • 39. Tronci, R., Muntoni, D., Fadda, G., et al: ‘Fusion of multiple clues for photo-attack detection in face recognition systems’. 2011 Int. Joint Conf. Biometrics (IJCB), 2011, pp. 16.
    40. 40)
      • 40. Zhang, Z., Yan, J., Liu, S., et al: ‘A face antispoofing database with diverse attacks’. 2012 5th IAPR Int. Conf. Biometrics (ICB), 2012, pp. 2631.
    41. 41)
      • 41. Tan, R.T., Ikeuchi, K.: ‘Separating reflection components of textured surfaces using a single image’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (2), pp. 178193.
    42. 42)
      • 42. Bai, J., Ng, T.-T., Gao, X., et al: ‘Is physics-based liveness detection truly possible with a single image?’. Proc. of 2010 IEEE Int. Symp. Circuits and Systems (ISCAS), 2010, pp. 34253428.
    43. 43)
      • 43. Boulkenafet, Z., Komulainen, J., Hadid, A.: ‘Face anti-spoofing based on color texture analysis’. 2015 IEEE Int. Conf. Image Processing (ICIP), 2015, pp. 26362640.
    44. 44)
      • 44. Galbally, J., Marcel, S., Fierrez, J.: ‘Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition’, IEEE Trans. Image Process., 2014, 23, (2), pp. 710724. IQA.
    45. 45)
      • 45. Kim, G., Eum, S., Suhr, J.K., et al: ‘Face liveness detection based on texture and frequency analyses’. 2012 5th IAPR Int. Conf. Biometrics (ICB), 2012, pp. 6772.
    46. 46)
      • 46. Kose, N., Dugelay, J.-L.: ‘Classification of captured and recaptured images to detect photograph spoofing’. 2012 Int. Conf. Informatics, Electronics & Vision (ICIEV), 2012, pp. 10271032.
    47. 47)
      • 47. Komulainen, J., Hadid, A., Pietikäinen, M.: ‘Face spoofing detection using dynamic texture’. Computer Vision-ACCV 2012 Workshops, 2013, pp. 146157.
    48. 48)
      • 48. Tirunagari, S., Poh, N., Windridge, D., et al: ‘Detection of face spoofing using visual dynamics’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 762777.
    49. 49)
      • 49. Chingovska, I., Rabello dos Anjos, A.: ‘On the use of client identity information for face antispoofing’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 787796.
    50. 50)
      • 50. Yang, J., Lei, Z., Yi, D., et al: ‘Person-specific face antispoofing with subject domain adaptation’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 797809.
    51. 51)
      • 51. Hiscocks, P.: ‘Measuring luminance with a digital camera’, 2011.
    52. 52)
      • 52. Lam, E.Y., Fung, G.S.K.: ‘Automatic white balancing in digital photography’, Single Sens. Imaging, Methods Appl. Digit. Cameras, 2008, pp. 267294.
    53. 53)
      • 53. Schroff, F., Kalenichenko, D., Philbin, J.: ‘FaceNet: a unified embedding for face recognition and clustering’, arXiv:1503.03832 [cs], June 2015, pp. 815823.
    54. 54)
      • 54. Baltru, T., Robinson, P., Morency, L.-P., et al: ‘OpenFace: an open source facial behavior analysis toolkit’. 2016 IEEE Winter Conf. Applications of Computer Vision (WACV), 2016, pp. 110.
    55. 55)
      • 55. Baltrusaitis, T., Robinson, P., Morency, L.-P.: ‘Constrained local neural fields for robust facial landmark detection in the wild’. Proc. of the IEEE Int. Conf. Computer Vision Workshops, 2013, pp. 354361.
    56. 56)
      • 56. Pitie, F., Kokaram, A.C., Dahyot, R.: ‘N-dimensional probability density function transfer and its application to color transfer’. Tenth IEEE Int. Conf. Computer Vision, 2005, ICCV 2005, 2005, vol. 2, pp. 14341439.
    57. 57)
      • 57. Rabin, J., Delon, J., Gousseau, Y.: ‘Removing artefacts from color and contrast modifications’, IEEE Trans. Image Process., 2011, 20, (11), pp. 30733085.
    58. 58)
      • 58. Moré, J.J.: ‘The Levenberg-Marquardt algorithm: implementation and theory’ (Springer Berlin Heidelberg, Berlin, Heidelberg, 1978), pp. 105116.
    59. 59)
      • 59. ISO/IEC 2382-37:2017(E). ISO/IEC 2382-37:2017(E): https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:-37:ed-2:v1:en, 2017.
    60. 60)
      • 60. ISO/IEC 30107-1:2016(E). ISO/IEC 30107-1:2016(E): https://www.iso.org/obp/ui/#iso:std:iso-iec:30107:-1:ed-1:v1:en, 2016.
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