Skip to main content
SpringerLink
Log in

Attended-Auxiliary Supervision Representation for Face Anti-spoofing

  • Conference paper
  • First Online:
Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12622))

Included in the following conference series:

Abstract

Recent face anti-spoofing methods have achieved impressive performance in recognizing the subtle discrepancies between live and spoof faces. However, due to directly holistic extraction and the resulting ineffective clues used for the models’ perception, the previous methods are still subject to setbacks of not being generalizable to the diversity of presentation attacks. In this paper, we present an attended-auxiliary supervision approach for radical exploitation, which automatically concentrates on the most important regions of the input, that is, those that make significant contributions towards distinguishing the spoof cases from live faces. Through a multi-task learning approach, the proposed network is able to locate the most relevant/attended/highly selective regions more accurately than previous methods, leading to notable improvements in performance. We also suggest that introducing spatial attention mechanisms can greatly enhance our model’s perception of the important information, partly intensifying the resilience of our model against diverse types of face anti-spoofing attacks. We carried out extensive experiments on publicly available face anti-spoofing datasets, showing that our approach and hypothesis converge to some extent and demonstrating state-of-the-art performance.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10, 746–761 (2015)

    Article  Google Scholar 

  2. Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In, : BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), vol. 2012, pp. 1–7. IEEE (2012)

    Google Scholar 

  3. Kim, W., Suh, S., Han, J.J.: Face liveness detection from a single image via diffusion speed model. IEEE Trans. Image Process. 24, 2456–2465 (2015)

    Article  MathSciNet  Google Scholar 

  4. 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, 548–558 (2007)

    Article  Google Scholar 

  5. Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic web camera. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8. IEEE (2007)

    Google Scholar 

  6. Luo, S., Kan, M., Wu, S., Chen, X., Shan, S.: Face anti-spoofing with multi-scale information. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3402–3407. IEEE (2018)

    Google Scholar 

  7. Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 319–328. IEEE (2017)

    Google Scholar 

  8. Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 389–398 (2018)

    Google Scholar 

  9. Yang, X., et al.: Face anti-spoofing: model matters, so does data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3507–3516 (2019)

    Google Scholar 

  10. Kim, T., Kim, Y., Kim, I., Kim, D.: Basn: enriching feature representation using bipartite auxiliary supervisions for face anti-spoofing. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  11. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  12. Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: International Joint Conference on Biometrics (IJCB), vol. 2011, pp. 1–7. IEEE (2011)

    Google Scholar 

  13. de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBP - TOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37410-4_11

    Chapter  Google Scholar 

  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: International Conference on Biometrics (ICB), vol. 2013, pp. 1–8. IEEE (2013)

    Google Scholar 

  15. Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection with component dependent descriptor. In: 2013 International Conference on Biometrics (ICB), pp. 1–6. IEEE (2013)

    Google Scholar 

  16. Komulainen, J., Hadid, A., Pietikäinen, 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 

  17. Patel, K., Han, H., Jain, A.K.: Secure face unlock: spoof detection on smartphones. IEEE Trans. Inf. Forensics Secur. 11, 2268–2283 (2016)

    Article  Google Scholar 

  18. Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: IEEE International Conference on Image Processing (ICIP), vol. 2015, pp. 2636–2640. IEEE (2015)

    Google Scholar 

  19. Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using color texture analysis. IEEE Trans. Inf. Forensics Secur. 11, 1818–1830 (2016)

    Article  Google Scholar 

  20. 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 

  21. Siddiqui, T.A., et al.: Face anti-spoofing with multi feature videolet aggregation. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 1035–1040. IEEE (2016)

    Google Scholar 

  22. Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Biometric Technology for Human Identification, vol. 5404, pp. 296–303, International Society for Optics and Photonics (2004)

    Google Scholar 

  23. Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 252–260. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_27

    Chapter  Google Scholar 

  24. Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE (2016)

    Google Scholar 

  25. Patel, K., Han, H., Jain, A.K.: Cross-database face antispoofing with robust feature representation. In: You, Z., et al. (eds.) CCBR 2016. LNCS, vol. 9967, pp. 611–619. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46654-5_67

    Chapter  Google Scholar 

  26. Jourabloo, A., Liu, Y., Liu, X.: Face de-spoofing: anti-spoofing via noise modeling. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 290–306 (2018)

    Google Scholar 

  27. Liu, Y., Jourabloo, A., Ren, W., Liu, X.: Dense face alignment. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1619–1628 (2017)

    Google Scholar 

  28. Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)

    Google Scholar 

  29. Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3D face reconstruction and dense alignment with position map regression network. In: ECCV (2018)

    Google Scholar 

  30. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  31. Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: Oulu-npu: a mobile face presentation attack database with real-world variations. In: 2017 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), pp. 612–618. IEEE (2017)

    Google Scholar 

  32. ISO/IEC JTC 1/SC 37 Biometrics.: information technology biometric presentation attack detection part 1: Framework (2006). https://www.iso.org/obp/ui/iso

  33. Huang, Y., Zhang, W., Wang, J.: Deep frequent spatial temporal learning for face anti-spoofing. arXiv preprint arXiv:2002.03723 (2020)

  34. Boulkenafet, Z., et al.: A competition on generalized software-based face presentation attack detection in mobile scenarios. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 688–696. IEEE (2017)

    Google Scholar 

  35. Maaten, L.v., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Science and Engineering Teikyo University, Utsunomiya, Tochigi, 320-8551, Japan

    Son Minh Nguyen, Linh Duy Tran & Masayuki Arai

Authors
  1. Son Minh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linh Duy Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masayuki Arai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Son Minh Nguyen .

Editor information

Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen, S.M., Tran, L.D., Arai, M. (2021). Attended-Auxiliary Supervision Representation for Face Anti-spoofing. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12622. Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69525-5_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69524-8

  • Online ISBN: 978-3-030-69525-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation