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Review of Face Presentation Attack Detection Competitions

  • Jukka KomulainenEmail author
  • Zinelabidine Boulkenafet
  • Zahid Akhtar
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Face presentation attack detection has received increasing attention ever since the vulnerabilities to spoofing have been widely recognized. The state of the art in software-based face anti-spoofing has been assessed in three international competitions organized in conjunction with major biometrics conferences in 2011, 2013, and 2017, each introducing new challenges to the research community. In this chapter, we present the design and results of the three competitions. The particular focus is on the latest competition, where the aim was to evaluate the generalization abilities of the proposed algorithms under some real-world variations faced in mobile scenarios, including previously unseen acquisition conditions, presentation attack instruments, and sensors. We also discuss the lessons learnt from the competitions and future challenges in the field in general.

Notes

Acknowledgements

The financial support from the Finnish Foundation for Technology Promotion and Infotech Oulu Doctoral Program is acknowledged.

References

  1. 1.
    International Organization for Standardization (2016) ISO/IEC JTC 1/SC 37 biometrics: information technology—biometric presentation attack detection—part 1: framework. Technical reportGoogle Scholar
  2. 2.
    Chingovska I, Erdogmus N, Anjos A, Marcel S (2016) Face recognition systems under spoofing attacks. In: Bourlai T (ed) Face recognition across the imaging spectrum. Springer International Publishing, pp 165–194Google Scholar
  3. 3.
    Li Y, Li Y, Xu K, Yan Q, Deng R (2016) Empirical study of face authentication systems under OSNFD attacks. IEEE Trans Dependable Secur ComputGoogle Scholar
  4. 4.
    Mohammadi A, Bhattacharjee S, Marcel S (2018) Deeply vulnerable: a study of the robustness of face recognition to presentation attacks. IET Biom 7(1):15–26CrossRefGoogle Scholar
  5. 5.
    Boulkenafet Z, Komulainen J, Li L, Feng X, Hadid A (2017) OULU-NPU: a mobile face presentation attack database with real-world variations. In: IEEE International conference on automatic face and gesture recognitionGoogle Scholar
  6. 6.
    Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing. In: International conference of the biometrics special interest group (BIOSIG), pp 1–7Google Scholar
  7. 7.
    Costa-Pazo A, Bhattacharjee S, Vazquez-Fernandez E, Marcel S (2016) The REPLAY-MOBILE face presentation-attack database. In: International conference on biometrics special interests group (BIOSIG)Google Scholar
  8. 8.
    Tan X, Li Y, Liu J, Jiang L (2010) Face liveness detection from a single image with sparse low rank bilinear discriminative model. Springer, Berlin, Heidelberg, pp 504–517.  https://doi.org/10.1007/978-3-642-15567-3_37CrossRefGoogle Scholar
  9. 9.
    Wen D, Han H, Jain A (2015) Face spoof detection with image distortion analysis. Trans Inf Forensics Secur 10(4):746–761CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Boulkenafet Z, Komulainen J, Akhtar Z, Benlamoudi A, Samai D, Bekhouche S, Ouafi A, Dornaika F, Taleb-Ahmed A, Qin L, Peng F, Zhang L, Long M, Bhilare S, Kanhangad V, Costa-Pazo A, Vazquez-Fernandez E, Perez-Cabo D, Moreira-Perez JJ, Gonzalez-Jimenez D, Mohammadi A, Bhattacharjee S, Marcel S, Volkova S, Tang Y, Abe N, Li L, Feng X, Xia Z, Jiang X, Liu S, Shao 0R, Yuen PC, Almeida WR, Andalo F, Padilha R, Bertocco G, Dias W, Wainer J, Torres R, Rocha A, Angeloni MA, Folego G, Godoy A, Hadid A (2017) A competition on generalized software-based face presentation attack detection in mobile scenarios. In: IEEE international joint conference on biometrics (IJCB)Google Scholar
  12. 12.
    Chakka 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, Li S, Schwartz W, Rocha A, Pedrini H, Lorenzo-Navarro J, Castrillon-Santana M, Määttä J, Hadid A, Pietikäinen M (2011) Competition on counter measures to 2-D facial spoofing attacks. In: International joint conference on biometrics (IJCB)Google Scholar
  13. 13.
    Chingovska I, Yang J, Lei Z, Yi D, Li SZ, Kähm O, Glaser C, Damer N, Kuijper A, Nouak A, Komulainen J, Pereira T, Gupta S, Khandelwal S, Bansal S, Rai A, Krishna T, Goyal D, Waris MA, Zhang H, Ahmad I, Kiranyaz S, Gabbouj M, Tronci R, Pili M, Sirena N, Roli F, Galbally J, Fierrez J, Pinto A, Pedrini H, Schwartz WS, Rocha A, Anjos A, Marcel S (2013) The 2nd competition on counter measures to 2D face spoofing attacks. In: International conference on biometrics (ICB)Google Scholar
  14. 14.
    Erdogmus N, Marcel S (2013) Spoofing attacks to 2D face recognition systems with 3D masks. In: IEEE international conference of the biometrics special interest groupGoogle Scholar
  15. 15.
    Raghavendra R, Raja KB, Busch C (2015) Presentation attack detection for face recognition using light field camera. IEEE Trans Image Process 24(3):1060–1075MathSciNetCrossRefGoogle Scholar
  16. 16.
    Pavlidis I, Symosek P (2000) The imaging issue in an automatic face/disguise detection system. In: IEEE workshop on computer vision beyond the visible spectrum: methods and applications (CVBVS), pp 15–24Google Scholar
  17. 17.
    Rudd EM, Günther M, Boult TE (2016) PARAPH: presentation attack rejection by analyzing polarization hypotheses. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 171 – 178.  https://doi.org/10.1109/CVPRW.2016.28
  18. 18.
    Zhang Z, Yi D, Lei Z, Li SZ (2011) Face liveness detection by learning multispectral reflectance distributions. In: International conference on face and gesture, pp 436–441Google Scholar
  19. 19.
    Kollreider K, Fronthaler H, Faraj MI, Bigun J (2007) Real-time face detection and motion analysis with application in liveness assessment. IEEE Trans Inf Forensics Secur 2(3):548–558CrossRefGoogle Scholar
  20. 20.
    Ng ES, Chia AYS (2012) Face verification using temporal affective cues. In: International conference on pattern recognition (ICPR), pp 1249–1252Google Scholar
  21. 21.
    Chetty G, Wagner M (2004) Liveness verification in audio-video speaker authentication. In: Australian international conference on speech science and technology, pp 358–363Google Scholar
  22. 22.
    Frischholz RW, Werner A (2003) Avoiding replay-attacks in a face recognition system using head-pose estimation. In: IEEE international workshop on analysis and modeling of faces and gesturesGoogle Scholar
  23. 23.
    De Marsico M, Nappi M, Riccio D, Dugelay JL (2012) Moving face spoofing detection via 3D projective invariants. In: IAPR international conference on biometrics (ICB)Google Scholar
  24. 24.
    Wang T, Yang J, Lei Z, Liao S, Li SZ (2013) Face liveness detection using 3D structure recovered from a single camera. In: International conference on biometrics (ICB)Google Scholar
  25. 25.
    Li J, Wang Y, Tan T, Jain AK (2004) Live face detection based on the analysis of Fourier spectra. In: Proceedings of biometric technology for human identification, pp 296–303Google Scholar
  26. 26.
    Bai J, Ng TT, Gao X, Shi YQ (2010) Is physics-based liveness detection truly possible with a single image? In: IEEE international symposium on circuits and systems (ISCAS), pp 3425–3428Google Scholar
  27. 27.
    Kose N, Dugelay JL (2013) Countermeasure for the protection of face recognition systems against mask attacks. In: International conference on automatic face and gesture recognition (FG)Google Scholar
  28. 28.
    Määttä J, Hadid A, Pietikäinen M (2011) Face spoofing detection from single images using micro-texture analysis. In: Proceedings of international joint conference on biometrics (IJCB).  https://doi.org/10.1109/IJCB.2011.6117510
  29. 29.
    Yang J, Lei Z, Liao S, Li SZ (2013) Face liveness detection with component dependent descriptor. In: International conference on biometrics (ICB)Google Scholar
  30. 30.
    Feng L, Po LM, Li Y, Xu X, Yuan F, Cheung TCH, Cheung KW (2016) Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J Vis Commun Image Represent 38:451–460CrossRefGoogle Scholar
  31. 31.
    Galbally J, Marcel S (2014) Face anti-spoofing based on general image quality assessment. In: IAPR/IEEE international conference on pattern recognition (ICPR), pp 1173–1178Google Scholar
  32. 32.
    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
  33. 33.
    Bharadwaj S, Dhamecha TI, Vatsa M, Richa S (2013) Computationally efficient face spoofing detection with motion magnification. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Google Scholar
  34. 34.
    Pan G, Wu Z, Sun L (2008) Liveness detection for face recognition. In: Proceedings of recent advances in face recognition, pp 109–124. In-TehGoogle Scholar
  35. 35.
    Siddiqui T, Bharadwaj S, Dhamecha T, Agarwal A, Vatsa M, Singh R, Ratha N (2016) Face anti-spoofing with multifeature videolet aggregation. In: International conference on pattern recognition (ICPR)Google Scholar
  36. 36.
    Tirunagari S, Poh N, Windridge D, Iorliam A, Suki N, Ho ATS (2015) Detection of face spoofing using visual dynamics. IEEE Trans Inf Forensics Secur 10(4):762–777CrossRefGoogle Scholar
  37. 37.
    Li X, Komulainen J, Zhao G, Yuen PC, Pietikäinen M (2016) Generalized face anti-spoofing by detecting pulse from face videos. In: International conference on pattern recognition (ICPR)Google Scholar
  38. 38.
    Liu S, Yuen PC, Zhang S, Zhao G (2016) 3D mask face anti-spoofing with remote photoplethysmography. In: European conference on computer vision (ECCV). Springer International Publishing, pp 85–100Google Scholar
  39. 39.
    de Freitas Pereira T, Anjos A, De Martino J, Marcel S (2013) Can face anti-spoofing countermeasures work in a real world scenario? In: International conference on biometrics (ICB)Google Scholar
  40. 40.
    Boulkenafet Z, Komulainen J, Hadid A (2015) Face anti-spoofing based on color texture analysis. In: IEEE international conference on image processing (ICIP)Google Scholar
  41. 41.
    Boulkenafet Z, Komulainen J, Hadid A (2016) Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Sig Process Lett 24(2):141–145Google Scholar
  42. 42.
    Boulkenafet Z, Komulainen J, Hadid A (2016) Face spoofing detection using colour texture analysis. IEEE Trans Inf Forensics Secur 11(8):1818–1830CrossRefGoogle Scholar
  43. 43.
    Boulkenafet Z, Komulainen J, Hadid A (2018) On the generalization of color texture-based face anti-spoofing. Image Vis ComputGoogle Scholar
  44. 44.
    Patel K, Han H, Jain AK (2016) Cross-database face antispoofing with robust feature representation. In: Chinese conference on biometric recognition (CCBR), pp 611–619CrossRefGoogle Scholar
  45. 45.
    Pinto A, Pedrini H, Robson Schwartz W, Rocha A (2015) Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans Image Process 24(12):4726–4740MathSciNetCrossRefGoogle Scholar
  46. 46.
    Yang J, Lei Z, Li SZ (2014) Learn convolutional neural network for face anti-spoofing. CoRR. http://arxiv.org/abs/1408.5601
  47. 47.
    Manjani I, Tariyal S, Vatsa M, Singh R, Majumdar A (2017) Detecting silicone mask based presentation attack via deep dictionary learning. IEEE Trans Inf Forensics SecurGoogle Scholar
  48. 48.
    Anjos A, Marcel S (2011) 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)Google Scholar
  49. 49.
    Ojala T, Pietikäinen M, Mäenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  50. 50.
    Korshunov P, Marcel S, Muckenhirn H, Gonçalves AR, Mello AGS, Violato RPV, Simoes FO, Neto MU, de Assis Angeloni M, Stuchi JA, Dinkel H, Chen N, Qian Y, Paul D, Saha G, Sahidullah M (2016) Overview of BTAS 2016 speaker anti-spoofing competition. In: IEEE international conference on biometrics theory, applications and systems (BTAS)Google Scholar
  51. 51.
    Yambay D, Becker B, Kohli N, Yadav D, Czajka A, Bowyer KW, Schuckers S, Singh R, Vatsa M, Noore A, Gragnaniello D, Sansone C, Verdoliva L, He L, Ru Y, Li H, Liu N, Sun Z, Tan T (2017) LivDet-Iris 2017—iris liveness detection competition 2017. In: IEEE international joint conference on biometrics (IJCB)Google Scholar
  52. 52.
    Yambay D, Doyle JS, Bowyer KW, Czajka A, Schuckers S (2014) LivDet-iris 2013—iris liveness detection competition 2013. In: IEEE international joint conference on biometrics (IJCB), pp 1–8Google Scholar
  53. 53.
    Galbally J, Marcel S, Fiérrez J (2014) Biometric antispoofing methods: a survey in face recognition. IEEE Access 2:1530–1552CrossRefGoogle Scholar
  54. 54.
    Chingovska I, Anjos A, Marcel S (2013) Anti-spoofing in action: joint operation with a verification system. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 98–104Google Scholar
  55. 55.
    Rattani A, Poh N, Ross A (2013) A bayesian approach for modeling sensor influence on quality, liveness and match score values in fingerprint verification. In: IEEE international workshop on information forensics and security (WIFS), pp 37–42Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jukka Komulainen
    • 1
    Email author
  • Zinelabidine Boulkenafet
    • 1
  • Zahid Akhtar
    • 2
  1. 1.Center for Machine Vision and Signal AnalysisUniversity of OuluOuluFinland
  2. 2.INRS-EMTUniversity of QuebecQuebec CityCanada

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