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Recent Progress on Face Presentation Attack Detection of 3D Mask Attacks

  • Si-Qi Liu
  • Pong C. YuenEmail author
  • Xiaobai Li
  • Guoying Zhao
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

With the advanced 3D reconstruction and printing technologies, creating a super-real 3D facial mask becomes feasible at an affordable cost. This brings a new challenge to face presentation attack detection (PAD) against 3D facial mask attack. As such, there is an urgent need to solve this problem as many face recognition systems have been deployed in real-world applications. Since this is a relatively new research problem, few studies has been conducted and reported. In order to attract more attentions on 3D mask face PAD, this book chapter summarizes the progress in the past few years, as well as publicly available datasets. Finally, some open problems in 3D mask attack are discussed.

Notes

Acknowledgements

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.
    Galbally J, Marcel S, Fierrez J (2014) Biometric antispoofing methods: a survey in face recognition. IEEE Access 2:1530–1552CrossRefGoogle Scholar
  2. 2.
    Hadid A, Evans N, Marcel S, Fierrez J (2015) Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Process Mag 32(5):20–30CrossRefGoogle Scholar
  3. 3.
    Rattani A, Poh N, Ross A (2012) Analysis of user-specific score characteristics for spoof biometric attacks. In: CVPRWGoogle Scholar
  4. 4.
    Evans NW, Kinnunen T, Yamagishi J (2013) Spoofing and countermeasures for automatic speaker verification. In: Interspeech, pp 925–929Google Scholar
  5. 5.
    Pavlidis I, Symosek P (2000) The imaging issue in an automatic face/disguise detection system. In: Computer vision beyond the visible spectrum: methods and applicationsGoogle Scholar
  6. 6.
    Tan X, Li Y, Liu J, Jiang L (2010) Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Computer vision–ECCV, pp 504–517Google Scholar
  7. 7.
    Määttä J, Hadid A, Pietikäinen M (2011) Face spoofing detection from single images using micro-texture analysis. In: IJCBGoogle Scholar
  8. 8.
    Anjos A, Marcel S (2011) Counter-measures to photo attacks in face recognition: a public database and a baseline. In: International joint conference on biometrics (IJCB), pp 1–7Google Scholar
  9. 9.
    Zhang Z, Yan J, Liu S, Lei Z, Yi D, Li SZ (2012) A face antispoofing database with diverse attacks. In: International conference on biometrics (ICB), pp 26–31Google Scholar
  10. 10.
    Pan G, Sun L, Wu Z, Lao S (2007) Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: ICCVGoogle Scholar
  11. 11.
    de Freitas Pereira T, Komulainen J, Anjos A, De Martino JM, Hadid A, Pietikäinen M, Marcel S (2014) Face liveness detection using dynamic texture. EURASIP J Image Video Process 2014(1):1–15Google Scholar
  12. 12.
    Kose N, Dugelay JL (2014) Mask spoofing in face recognition and countermeasures. Image Vis Comput 32(10):779–789CrossRefGoogle Scholar
  13. 13.
    Yi D, Lei Z, Zhang Z, Li SZ (2014) Face anti-spoofing: multi-spectral approach. Handbook of biometric anti-spoofing. Springer, Berlin, pp 83–102Google Scholar
  14. 14.
    Galbally J, Marcel S, Fierrez J (2014) Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710–724MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kose N, Dugelay JL (2013) Shape and texture based countermeasure to protect face recognition systems against mask attacks. In: CVPRWGoogle Scholar
  16. 16.
    Wen D, Han H, Jain AK (2015) Face spoof detection with image distortion analysis. IEEE Trans Inf Forensics Secur 10(4):746–761CrossRefGoogle Scholar
  17. 17.
    Erdogmus N, Marcel S (2014) Spoofing face recognition with 3D masks. IEEE Trans Inf Forensics Secur 9(7):1084–1097CrossRefGoogle Scholar
  18. 18.
    Wallace R, McLaren M, McCool C, Marcel S (2011) Inter-session variability modelling and joint factor analysis for face authentication. In: IJCBGoogle Scholar
  19. 19.
    Liu S, Yuen PC, Zhang S, Zhao G (2016) 3d mask face anti-spoofing with remote photoplethysmography. In: ECCVGoogle Scholar
  20. 20.
    Erdogmus N, Marcel S (2013) Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In: BTASGoogle Scholar
  21. 21.
    Liu S, Yang B, Yuen PC, Zhao G (2016) A 3D mask face anti-spoofing database with real world variations. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 100–106Google Scholar
  22. 22.
    Manjani I, Tariyal S, Vatsa M, Singh R, Majumdar A (2017) Detecting silicone mask based presentation attack via deep dictionary learning. In: TIFSGoogle Scholar
  23. 23.
    Agarwal A, Yadav D, Kohli N, Singh R, Vatsa M, Noore A (2017) Face presentation attack with latex masks in multispectral videos. SMAD 13:130Google Scholar
  24. 24.
    Boulkenafet Z, Komulainen J, Hadid A (2016) Face spoofing detection using colour texture analysis. IEEE Trans Inf Forensics Secur 11(8):1818–1830CrossRefGoogle Scholar
  25. 25.
    de Freitas Pereira T, Anjos A, De Martino JM, Marcel S (2013) Can face anti-spoofing countermeasures work in a real world scenario? In: International conference on biometrics (ICB), pp 1–8Google Scholar
  26. 26.
    Agarwal A, Singh R, Vatsa M (2016) Face anti-spoofing using Haralick features. In: BTASGoogle Scholar
  27. 27.
    Haralick RM, Shanmugam K et al (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 1(6):610–621CrossRefGoogle Scholar
  28. 28.
    Cohen-Steiner D, Morvan JM (2003) Restricted delaunay triangulations and normal cycle. In: Proceedings of the nineteenth annual symposium on computational geometry, pp 312–321Google Scholar
  29. 29.
    Tang Y, Chen L (2017) 3d facial geometric attributes based anti-spoofing approach against mask attacks. In: FGGoogle Scholar
  30. 30.
    Menotti D, Chiachia G, Pinto A, Schwartz WR, Pedrini H, Falcão AX, Rocha A (2015) Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans Inf Forensics Secur 10(4):864–879CrossRefGoogle Scholar
  31. 31.
    Yang J, Lei Z, Li SZ (2014) Learn convolutional neural network for face anti-spoofing. arXiv:1408.5601
  32. 32.
    Tariyal S, Majumdar A, Singh R, Vatsa M (2016) Deep dictionary learning. IEEE Access 4:10096–10109CrossRefGoogle Scholar
  33. 33.
    Chaudhry R, Ravichandran A, Hager G, Vidal R (2009) Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: CVPRGoogle Scholar
  34. 34.
    Siddiqui TA, Bharadwaj S, Dhamecha TI, Agarwal A, Vatsa M, Singh R, Ratha N (2016) Face anti-spoofing with multifeature videolet aggregation. In: ICPRGoogle Scholar
  35. 35.
    Rui Shao XL, Yuen PC (2017) Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3d mask face anti-spoofing. In: IJCBGoogle Scholar
  36. 36.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR. arXiv:1409.1556
  37. 37.
    Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput VisGoogle Scholar
  38. 38.
    Li X, Komulainen J, Zhao G, Yuen PC, Pietikäinen M (2016) Generalized face anti-spoofing by detecting pulse from face videos. In: 23rd international conference on pattern recognition (ICPR). IEEE, pp 4244–4249Google Scholar
  39. 39.
    Shelley K, Shelley S (2001) Pulse oximeter waveform: photoelectric plethysmography. In: Lake C, Hines R, Blitt C (eds) Clinical monitoring. WB Saunders Company, Philadelphia, pp 420–428Google Scholar
  40. 40.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: CVPRGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Si-Qi Liu
    • 1
  • Pong C. Yuen
    • 1
    Email author
  • Xiaobai Li
    • 2
  • Guoying Zhao
    • 2
  1. 1.Department of Computer ScienceHong Kong Baptist UniversityKowloonHong Kong
  2. 2.Center for Machine Vision and Signal AnalysisUniversity of OuluOuluFinland

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