Introduction to Face Presentation Attack Detection

  • Javier Hernandez-OrtegaEmail author
  • Julian Fierrez
  • Aythami Morales
  • Javier Galbally
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The main scope of this chapter is to serve as a brief introduction to face presentation attack detection. The next pages present the different presentation attacks that a face recognition system can confront, in which an attacker presents to the sensor, mainly a camera, an artifact (generally a photograph, a video, or a mask) to try to impersonate a genuine user. First, we make an introduction of the current status of face recognition, its level of deployment, and the challenges it faces. In addition, we present the vulnerabilities and the possible attacks that a biometric system may be exposed to, showing that way the high importance of presentation attack detection methods. We review different types of presentation attack methods, from simpler to more complex ones, and in which cases they could be effective. Later, we summarize the most popular presentation attack detection methods to deal with these attacks. Finally, we introduce public datasets used by the research community for exploring the vulnerabilities of face biometrics and developing effective countermeasures against known spoofs.



This work was done in the context of the TABULA RASA and BEAT projects funded under the 7th Framework Programme of EU, the project CogniMetrics (TEC2015-70627-R), the COST Action CA16101 (Multi-Foresee), and project Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017). Author J. H.-O. is supported by a FPI Fellowship from Universidad Autonoma de Madrid.


  1. 1.
    Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Computer society conference on computer vision and pattern recognition (CVPR), pp 586–591Google Scholar
  2. 2.
    Biometrics: Market Shares, Strategies, and Forecasts, Worldwide, 2015–2021 (2015). Wintergreen Research, IncGoogle Scholar
  3. 3.
    Gipp B, Beel J, Rössling I (2007) ePassport: The Worlds New Electronic Passport. Risks and its Security. CreateSpace, A Report about the ePassports BenefitsGoogle Scholar
  4. 4.
    Garcia C (2004) Utilización de la firma electrónica en la Administración española iv: Identidad y firma digital. El DNI electrónico, Administración electrónica y procedimiento administrativoGoogle Scholar
  5. 5.
    Jain AK, Li SZ (2011) Handbook of face recognition. Springer, BerlinGoogle Scholar
  6. 6.
    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
  7. 7.
    Galbally J, Marcel S, Fierrez J (2014) Biometric antispoofing methods: a survey in face recognition. IEEE Access 2:1530–1552CrossRefGoogle Scholar
  8. 8.
    Gomez-Barrero M, Galbally J, Fierrez J, Ortega-Garcia J (2013) Multimodal biometric fusion: a study on vulnerabilities to indirect attacks. In: Iberoamerican congress on pattern recognition, Springer, Berlin, pp 358–365CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Goodin D (2008) Get your german interior ministers fingerprint here. Register 30Google Scholar
  11. 11.
    Tan X, Li Y, Liu J, Jiang L (2010) Face liveness detection from a single image with sparse low rank bilinear discriminative model. Comput Vis–ECCV 504–517Google Scholar
  12. 12.
    Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing. In: IEEE BIOSIGGoogle Scholar
  13. 13.
    Erdogmus N, Marcel S (2014) Spoofing face recognition with 3D masks. IEEE Trans Inf Forensics Secur 9(7):1084–1097CrossRefGoogle Scholar
  14. 14.
    Gonzalez-Sosa E, Vera-Rodriguez R, Fierrez J, Patel V (2018) Person recognition beyond the visible spectrum: combining body shape and texture from mmW images. In: International conference on biometrics (ICB)Google Scholar
  15. 15.
    Proceedings of the IEEE international conference acoust. speech signal process. (ICASSP) (2017)Google Scholar
  16. 16.
    Proceedings of the IEEE/IAPR international joint conference biometrics (IJCB) (2017)Google Scholar
  17. 17.
    Chingovska I, Yang J, Lei Z, Yi D, Li SZ, Kahm O, Glaser C, Damer N, Kuijper A, Nouak A et al (2013) The 2nd competition on counter measures to 2D face spoofing attacks. In: International conference on biometrics (ICB)Google Scholar
  18. 18.
    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
  19. 19.
    ISO: Information technology security techniques security evaluation of biometrics, ISO/IEC Standard ISO/IEC 19792:2009, 2009. International organization for standardization (2009).
  20. 20.
    ISO: Information technology – biometric presentation attack detection – Part 1: Framework. international organization for standardization (2016).
  21. 21.
    Kim J, Choi H, Lee W (2011) Spoof detection method for touchless fingerprint acquisition apparatus. Korea Patent 1(054):314Google Scholar
  22. 22.
    Dantcheva A, Chen C, Ross A (2012) Can facial cosmetics affect the matching accuracy of face recognition systems? In: 2012 IEEE Fifth international conference on biometrics: theory, applications and systems (BTAS), IEEE, pp 391–398Google Scholar
  23. 23.
    Anjos A, Chakka MM, Marcel S (2013) Motion-based counter-measures to photo attacks in face recognition. IET Biom 3(3):147–158CrossRefGoogle Scholar
  24. 24.
    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
  25. 25.
    Nguyen D, Bui Q (2009) Your face is NOT your password. BlackHat DCGoogle Scholar
  26. 26.
    da Silva Pinto A, Pedrini H, Schwartz W, Rocha A (2012) Video-based face spoofing detection through visual rhythm analysis. In: SIBGRAPI conference on graphics, patterns and images, pp 221–228Google Scholar
  27. 27.
    Kim Y, Yoo JH, Choi K (2011) A motion and similarity-based fake detection method for biometric face recognition systems. IEEE Trans Consum Electron 57(2):756–762CrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    Galbally J, Satta R (2016) Three-dimensional and two-and-a-half-dimensional face recognition spoofing using three-dimensional printed models. IET Biom 5(2):83–91CrossRefGoogle Scholar
  30. 30.
  31. 31.
    Kose N, Dugelay JL (2013) On the vulnerability of face recognition systems to spoofing mask attacks. In: (ICASSP) International conference on acoustics, speech and signal processing, IEEE, pp 2357–2361Google Scholar
  32. 32.
    Lagorio A, Tistarelli M, Cadoni M, Fookes C, Sridharan S (2013) Liveness detection based on 3D face shape analysis. In: International workshop on biometrics and forensics (IWBF), IEEEGoogle Scholar
  33. 33.
    Sun L, Huang W, Wu M (2011) TIR/VIS correlation for liveness detection in face recognition. In: International conference on computer analysis of images and patterns, Springer, Berlin, pp 114–121CrossRefGoogle Scholar
  34. 34.
    Kim Y, Na J, Yoon S, Yi J (2009) Masked fake face detection using radiance measurements. JOSA A 26(4):760–766CrossRefGoogle Scholar
  35. 35.
    Yang J, Lei Z, Liao S, Li SZ (2013) Face liveness detection with component dependent descriptor. In: International conference on biometrics (ICB), pp 1–6Google Scholar
  36. 36.
    Bharadwaj S, Dhamecha TI, Vatsa M, Singh R (2013) Computationally efficient face spoofing detection with motion magnification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 105–110Google Scholar
  37. 37.
    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
  38. 38.
    Smith DF, Wiliem A, Lovell BC (2015) Face recognition on consumer devices: reflections on replay attacks. IEEE Trans Inf Forensics Secur 10(4):736–745CrossRefGoogle Scholar
  39. 39.
    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
  40. 40.
    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 gesture recognition, pp 612–618Google Scholar
  41. 41.
    Hernandez-Ortega J, Fierrez J, Morales A, Tome P (2018) Time analysis of pulse-based face anti-spoofing in visible and NIR. In: IEEE CVPR computer society workshop on biometricsGoogle Scholar
  42. 42.
    Zhang D, Ding D, Li J, Liu Q (2015) PCA based extracting feature using fast fourier transform for facial expression recognition. In: Transactions on engineering technologies, pp 413–424Google Scholar
  43. 43.
    Gonzalez-Sosa E, Vera-Rodriguez R, Fierrez J, Patel V (2017) Exploring body shape from mmW images for person recognition. IEEE Trans Inf Forensics Secur 12(9):2078–2089CrossRefGoogle Scholar
  44. 44.
    Pan G, Wu Z, Sun L (2008) Liveness detection for face recognition. In: Recent advances in face recognition. InTechGoogle Scholar
  45. 45.
    Wu HY, Rubinstein M, Shih E, Guttag J, Durand F, Freeman W (2012) Eulerian video magnification for revealing subtle changes in the world. ACM Trans Graph 31(4)CrossRefGoogle Scholar
  46. 46.
    Boulkenafet Z, Komulainen J, Akhtar Z, Benlamoudi A, Samai D, Bekhouche S, Ouafi A, Dornaika F, Taleb-Ahmed A, Qin L, et al (2017) A competition on generalized software-based face presentation attack detection in mobile scenarios. In: International joint conference on biometrics (IJCB), pp 688–696Google Scholar
  47. 47.
    Chakka MM, 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 SZ, Schwartz WR, Rocha A, Pedrini H, Lorenzo-Navarro J, Castrilln-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
  48. 48.
    Ortega-Garcia J, Fierrez J, Alonso-Fernandez F, Galbally J, Freire MR, Gonzalez-Rodriguez J, Garcia-Mateo C, Alba-Castro JL, Gonzalez-Agulla E, Otero-Muras E (2010) The multiscenario multienvironment biosecure multimodal database (BMDB). IEEE Trans Pattern Anal Mach Intell 32(6):1097–1111CrossRefGoogle Scholar
  49. 49.
    Peixoto B, Michelassi C, Rocha A (2011) Face liveness detection under bad illumination conditions. In: International conference on image processing (ICIP), pp 3557–3560Google Scholar
  50. 50.
    Chingovska I, Anjos A, Marcel S (2013) Anti-spoofing in action: joint operation with a verification system. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 98–104Google Scholar
  51. 51.
    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
  52. 52.
    Fierrez J, Morales A, Vera-Rodriguez R, Camacho D (2018) Multiple classifiers in biometrics. Part 1: Fundamentals and review. Inf Fusion 44:57–64CrossRefGoogle Scholar
  53. 53.
    Ross AA, Nandakumar K, Jain AK (2006) Handbook of multibiometrics, SpringerGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Javier Hernandez-Ortega
    • 1
    Email author
  • Julian Fierrez
    • 2
  • Aythami Morales
    • 3
  • Javier Galbally
    • 4
  1. 1.Biometrics and Data Pattern Analytics - BiDA LabUniversidad Autonoma de MadridMadridSpain
  2. 2.Universidad Autonoma de MadridMadridSpain
  3. 3.School of EngineeringUniversidad Autonoma de MadridMadridSpain
  4. 4.European Commission - DG Joint Research CentreIspraItaly

Personalised recommendations