Skip to main content
SpringerLink
Log in
Book cover

Handbook of Biometric Anti-Spoofing pp 65–82Cite as

Face Anti-spoofing: Visual Approach

  • Chapter
  • First Online:

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

User authentication is an important step to protect information and in this regard face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work revealed that face biometrics is quite vulnerable to spoofing attacks. This chapter presents the different modalities of attacks to visual spectrum face recognition systems. We introduce public datasets for the evaluation of vulnerability of recognition systems and performance of countermeasures. Finally, we build a comprehensive view of antispoofing techniques for visual spectrum face recognition and provides an outlook of issues that remain unaddressed.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Schuckers SAC (2002) Spoofing and anti-spoofing measures. Information Security Technical Report 7:56–62

    Article  Google Scholar 

  2. Sun, T., Li, Q., Qiu, Z.: Advances in Biometric Person Authentication, chap. A Secure Multimodal Biomeric Verification Scheme, pp. 233–240. Springer (2005).

    Google Scholar 

  3. Rodrigues, R.N., Ling, L.L., Govindaraju, V.: Robustness of multimodal biometric fusion methods against spoof attacks. Journal of Visual Language and Computing (2009).

    Google Scholar 

  4. Galbally J, McCool C, Fierrez J, Marcel S, Ortega-Garcia J (2010) On the vulnerability of face verification systems to hill-climbing attacks. Pattern Recognition 43(3):1027–1038

    Article  MATH  Google Scholar 

  5. Jain, A.K., Flynn, P., Ross, A.A. (eds.): Handbook of Biometrics. Springer-Verlag (2008).

    Google Scholar 

  6. Duc, N.M., Minh, B.Q.: Your face is not your password. In: Black Hat Conference (2009).

    Google Scholar 

  7. Thalheim, L., Krissler, J., Ziegler, P.M.: Body check: Biometric access protection devices and their programs put to the test. Heise Online (2002).

    Google Scholar 

  8. Toth, B.: Biometric id card debates. Newsletter Biometrie (2005).

    Google Scholar 

  9. Pan, G., Wu, Z., Sun, L.: Liveness detection for face recognition. Recent Advances in Face Recognition pp. 109–124 (2008).

    Google Scholar 

  10. Ratha, N.K., Connell, J.H., Bolle, R.M.: An analysis of minutiae matching strength. In: Proceedings of the International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA), pp. 223–228. Springer-Verlag (2001).

    Google Scholar 

  11. Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer-Verlag (2011).

    Google Scholar 

  12. McCool, C., Marcel, S., Hadid, A., Pietikainen, M., Matejka, P., Cernocky, J., Poh, N., Kittler, J., Larcher, A., Levy, C., Matrouf, D., Bonastre, J.F., Tresadern, P., Cootes, T.: Bi-modal person recognition on a mobile phone: using mobile phone data. In: IEEE ICME Workshop on Hot Topics in Mobile Multimedia (2012).

    Google Scholar 

  13. Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Proceedings of the 11th European conference on Computer vision: Part VI, ECCV’10, pp. 504–517 (2010).

    Google Scholar 

  14. Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In. In Biometric Technology for Human Identification, pp. 296–303 (2004).

    Google Scholar 

  15. Bai, J., Ng, T.T., Gao, X., Shi, Y.Q.: Is physics-based liveness detection truly possible with a single image? In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 3425–3428 (2010).

    Google Scholar 

  16. Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB), Washington DC, USA (2011).

    Google Scholar 

  17. Anjos, A., Chakka, M.M., Marcel, S.: Motion-based counter-measures to photo attacks in face recognition (2013).

    Google Scholar 

  18. Chakka, M.M., Anjos, A., Marcel, S., Tronci, R., Muntoni, D., Fadda, G., Pili, M., Sirena, N., Murgia, G., Ristori, M., Roli, F., Yan, J., Yi, D., Lei, Z., Zhang, Z., Z.Li, S., Schwartz, W.R., Rocha, A., Pedrini, H., Lorenzo-Navarro, J., Castrillón-Santana, M., Määttä, J., Hadid, A., Pietikäinen, M.: Competition on counter measures to 2-d facial spoofing attacks. In: Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB), Washington DC, USA (2011).

    Google Scholar 

  19. Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: IEEE BIOSIG 2012 (2012).

    Google Scholar 

  20. Chingovska, I., Anjos, A., Marcel, S.: Anti-spoofing in action: joint operation with a verification system. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Biometrics (2013).

    Google Scholar 

  21. Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: ICB (2012).

    Google Scholar 

  22. Erdogmus, N., Marcel, S.: Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In: Biometrics: Theory, Applications and Systems Conference (BTAS’13) (2013).

    Google Scholar 

  23. Chetty, G., Wagner, M.: Liveness verification in audio-video speaker authentication. In: in Proc. 10th ASSTA conference, pp. 358–363. Macquarie University Press (2004).

    Google Scholar 

  24. Frischholz RW, Dieckmann U (2000) Bioid: A multimodal biometric identification system. Computer 33(2):64–68

    Article  Google Scholar 

  25. Kollreider K, Fronthaler H, Faraj MI, Bigun J (2007) Real-time face detection and motion analysis with application in liveness assessment. Trans. Info. For. Sec. 2(3):548–558

    Article  Google Scholar 

  26. 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 (CVBVS 2000), pp. 15–24. Washington, DC, USA (2000).

    Google Scholar 

  27. Sun L, Huang W, Wu M (2011) Tir/vis correlation for liveness detection in face recognition. Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II., CAIP’11Springer-Verlag, Berlin, Heidelberg, pp 114–121

    Google Scholar 

  28. Zhang, Z., Yi, D., Lei, Z., Li, S.Z.: Face liveness detection by learning multispectral reflectance distributions. In: International Conference on Face and Gesture, pp. 436–441 (2011).

    Google Scholar 

  29. Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.T.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. (SIGGRAPH 2012) 31(4) (2012).

    Google Scholar 

  30. Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Richa, S.: Computationally efficient face spoofing detection with motion magnification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Biometrics (2013).

    Google Scholar 

  31. Chaudhry, R., Ravich, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR (2009).

    Google Scholar 

  32. Kollreider K, Fronthaler H, Bigun J (2009) Non-intrusive liveness detection by face images. Image and Vision Computing 27:233–244

    Article  Google Scholar 

  33. Kollreider, K., Fronthaler, H., Bigun, J.: Verifying liveness by multiple experts in face biometrics. Computer Vision and Pattern Recognition Workshop pp. 1–6 (2008).

    Google Scholar 

  34. Bao, W., Li, H., Li, N., Jiang, W.: A liveness detection method for face recognition based on optical flow field. In: 2009 International Conference on Image Analysis and Signal Processing, pp. 233–236. IEEE (2009).

    Google Scholar 

  35. Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB), Washington DC, USA (2011).

    Google Scholar 

  36. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:971–987

    Article  Google Scholar 

  37. Kose, N., Dugelay, J.L.: Countermeasure for the protection of face recognition systems against mask attacks. In: FG 2013, IEEE International Conference on Automatic Face and Gesture Recognition, 22–26 April 2013, Shanghai, China. Shanghai, CHINA (2013).

    Google Scholar 

  38. Pereira, T.d.F., Komulainen, J., Anjos, A., De Martino, J.M., Hadid, A., Pietikäinen, M., Marcel, S.: Face liveness detection using dynamic texture. EURASIP Journal on Image and Video Processing (2013).

    Google Scholar 

  39. Zhao G, Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6):915–928

    Article  Google Scholar 

  40. Pinto, A.d.S., Pedrini, H., Schwartz, W.R., Rocha, A.: Video-based face spoofing detection through visual rhythm analysis. In: Conference on Graphics, Patterns and Images (Sibgrapi) (2012).

    Google Scholar 

  41. Wang, T., Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection using 3d structure recovered from a single camera. In: IAPR International Conference on Biometrics, ICB (2013).

    Google Scholar 

  42. Gao, X., Ng, T.T., Qiu, B., Chang, S.F.: Single-view recaptured image detection based on physics-based features. In: IEEE International Conference on Multimedia & Expo (ICME), pp. 1469–1474 (2010).

    Google Scholar 

  43. Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: in IEEE Workshop on Statistical Analysis in Computer Vision (2003).

    Google Scholar 

  44. Pan G, Sun L, Wu Z, Wang Y (2011) Monocular camera-based face liveness detection by combining eyeblink and scene context. Telecommunication Systems 47(3–4):215–225

    Article  Google Scholar 

  45. Komulainen, J., Hadid, A., Pietikäinen, M.: Context based face anti-spoofing. In: Proc. the IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS 2013), Washington, DC, 2013 (2013).

    Google Scholar 

  46. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. International Conference on Computer Vision & Pattern Recognition 2:886–893

    Google Scholar 

  47. Schwartz, W.R., Rocha, A., Pedrini, H.: Face Spoofing Detection through Partial Least Squares and Low-Level Descriptors. In: Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB), Washington DC, USA (2011).

    Google Scholar 

  48. Tronci, R., Muntoni, D., Fadda, G., Pili, M., Sirena, N., Murgia, G., Ristori, M., Roli, F.: Fusion of multiple clues for photo-attack detection in face recognition systems. In: Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB), Washington DC, USA (2011).

    Google Scholar 

  49. Yan, J., Zhang, Z., Lei, Z., Yi, D., Li, S.Z.: Face liveness detection by exploring multiple scenic clues. In: 12th International Conference on Control, Automation, Robotics and Vision, (ICARCV2012) (2012).

    Google Scholar 

  50. Komulainen, J., Anjos, A., Hadid, A., Marcel, S., Pietikäinen, M.: Complementary countermeasures for detecting scenic face spoofing attacks. In: IAPR International Conference on Biometrics, ICB (2013).

    Google Scholar 

  51. Ng, E.S., Chia, A.Y.S.: Face verification using temporal affective cues. In: International Conference on Pattern Recognition, ICPR, pp. 1249–1252 (2012).

    Google Scholar 

  52. Frischholz, R.W., Werner, A.: Avoiding replay-attacks in a face recognition systenm using head-pose estimation. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG ’03. IEEE Computer Society, Washington, DC, USA (2003).

    Google Scholar 

  53. De Marsico, M., Nappi, M., Riccio, D., Dugelay, J.L.: Moving face spoofing detection via 3D projective invariants. In: ICB 2012, 5th IAPR International Conference on Biometrics, 29 March-1 April 2012, New Delhi, India. New Delhi, INDIA (2012).

    Google Scholar 

  54. Liu C, Yuen J, Torralba A (2011) Sift flow: Dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33(5):978–994

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Swiss Innovation Agency (CTI Project Replay) and the TABULA RASA project (http://www.tabularasa-euproject.org) funded under the 7th Framework Programme of the European Union (EU) (grant agreement number 257289) for their financial support.

Author information

Authors and Affiliations

  1. Idiap Research Institute, rue Marconi 19, 1920, Martigny, Switzerland

    André Anjos & Sébastien Marcel

  2. Center for Machine Vision Research (CMV), Department of Computer Science and Engineering (CSE), University of Oulu, P.O. Box 4500, 90014, Oulu, Finland

    Jukka Komulainen, Abdenour Hadid & Matti Pietikäinen

Authors
  1. André Anjos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jukka Komulainen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sébastien Marcel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdenour Hadid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matti Pietikäinen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to André Anjos .

Editor information

Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, United Kingdom

    Mark S. Nixon

  3. Institute of Automation Center for Biometrics and Security Resea, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag London

About this chapter

Cite this chapter

Anjos, A., Komulainen, J., Marcel, S., Hadid, A., Pietikäinen, M. (2014). Face Anti-spoofing: Visual Approach. In: Marcel, S., Nixon, M., Li, S. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6524-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6524-8_4

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6523-1

  • Online ISBN: 978-1-4471-6524-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation