Skip to main content
SpringerLink
Log in

Survey of non-intrusive face spoof detection methods

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Biometrics are distinct physiological characteristics used to describe individuals. Compared to the traditional access control methods such as passwords and Person Identification Numbers (PIN) which can be forgotten and shared easily, biometrics are widely used in authentication systems. Even though the accuracy of face recognition systems is lower than that of the systems using fingerprint, iris, etc. as the acquisition devices of the latter evade the affine and photometric transformations, recognition systems with the face as a trait are widely used due to the contactless and non-intrusive nature of the acquisition device-camera. As the cameras are in-built in most of the handheld and portable devices such as mobile phones and laptops, the uncontrolled and/or unregulated immediacy of sharing the photographs via messaging services and uploading on social networks entices the attackers to create spoofs to deceive a face recognition system. Hence, it is necessary to incorporate a spoof detection algorithm in recognition systems before revealing the identity. This paper gives an overview of the steps involved in the face spoof detection process, the various databases available, the different measures to discern between live and spoof images, aligned with the perceived observance, the binary classifiers used, and the performance evaluation parameters revealed in the literature.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://parnec.nuaa.edu.cn/_upload/tpl/02/db/731/template731/pages/xtan/NUAAImposterDB_download.html

  2. http://biometrics.cse.msu.edu/pubs/databases.html

  3. http://biometrics.cse.msu.edu/pubs/databases.html

  4. www.idiap.ch/dataset/replayattack

  5. www.idiap.ch/dataset/printattack

  6. www.cbsr.ia.ac.cn/english/FaceAntiSpoofDatabases.asp

  7. The difference between the known response of the training data and the predictions made by the classifier on the training data. A higher value indicates lower classification accuracy and a lower value does not guarantee good predictive results for unseen data.

References

  1. Akhtar Z, Michelon C, Foresti G L (2014) Liveness detection for biometric authentication in mobile applications. In: 2014 International Carnahan conference on security technology (ICCST). IEEE, pp 1–6

  2. Akhtar Z, Micheloni C, Piciarelli C, Foresti G L (2014) Mobio_livdet: mobile biometric liveness detection. In: 2014 11th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, pp 187–192

  3. Alotaibi A, Mahmood A (2016) Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning. In: 2016 International conference on optoelectronics and image processing (ICOIP). IEEE, pp 1–5

  4. Amin R, Islam S H, Biswas G, Khan M K, Leng L, Kumar N (2016) Design of an anonymity-preserving three-factor authenticated key exchange protocol for wireless sensor networks. Comput Netw 101:42–62

    Article  Google Scholar 

  5. Angadi S A, Kagawade V C (2018) Detection of face spoofing using multiple texture descriptors. In: 2018 International conference on computational techniques, electronics and mechanical systems (CTEMS). IEEE, pp 151–156

  6. Atoum Y, Liu Y, Jourabloo A, Liu X (2017) Face anti-spoofing using patch and depth-based cnns. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 319–328

  7. Bai J, Ng T -T, Gao X, Shi Y -Q (2010) Is physics-based liveness detection truly possible with a single image?. In: Proceedings of 2010 IEEE international symposium on circuits and systems. IEEE, pp 3425–3428

  8. Bao W, Li H, Li N, Jiang W (2009) A liveness detection method for face recognition based on optical flow field. In: 2009 International conference on image analysis and signal processing. IEEE, pp 233–236

  9. Basri R, Jacobs D W (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233

    Article  Google Scholar 

  10. Benlamoudi A, Samai D, Ouafi A, Bekhouche S E, Taleb-Ahmed A, Hadid A (2015) Face spoofing detection using local binary patterns and fisher score. In: 2015 3rd International conference on control, engineering & information technology (CEIT). IEEE, pp 1–5

  11. Bhogal A P S, Söllinger D, Trung P, Uhl A (2017) Non-reference image quality assessment for biometric presentation attack detection. In: 2017 5th International workshop on biometrics and forensics (IWBF). IEEE, pp 1–6

  12. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  13. Chang C -C, Lin C -J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):1–27

    Article  Google Scholar 

  14. Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG-proceedings of the international conference of biometrics special interest group (BIOSIG). IEEE, pp 1–7

  15. Crete F, Dolmiere T, Ladret P, Nicolas M (2007) The blur effect: perception and estimation with a new no-reference perceptual blur metric. In: Human vision and electronic imaging XII, vol 6492. International Society for Optics and Photonics, p 64920I

  16. Cristianini N, Shawe-Taylor J, et al. (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  17. Dhawanpatil T, Joglekar B (2017) Face spoofing detection using multiscale local binary pattern approach. In: 2017 International conference on computing, communication, control and automation (ICCUBEA). IEEE, pp 1–5

  18. Dong J, Tian C, Xu Y (2017) Face liveness detection using color gradient features. In: 2017 International conference on security, pattern analysis, and cybernetics (SPAC). IEEE, pp 377–382

  19. Fisher R A (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7(2):179–188

    Article  Google Scholar 

  20. Galbally J, Marcel S (2014) Face anti-spoofing based on general image quality assessment. In: 2014 22nd International conference on pattern recognition. IEEE, pp 1173–1178

  21. Galbally J, Marcel S, Fierrez J (2013) Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710–724

    Article  MathSciNet  Google Scholar 

  22. Gao X, Ng T -T, Qiu B, Chang S -F (2010) Single-view recaptured image detection based on physics-based features. In: 2010 IEEE international conference on multimedia and expo. IEEE, pp 1469–1474

  23. Garcia D C, de Queiroz R L (2015) Face-spoofing 2d-detection based on moiré-pattern analysis. IEEE Trans Pattern Anal Mach Intell 10 (4):778–786

    Google Scholar 

  24. Garud D, Agrwal S (2016) Face liveness detection. In: 2016 International conference on automatic control and dynamic optimization techniques (ICACDOT). IEEE, pp 789–792

  25. Han H, Klare B F, Bonnen K, Jain A K (2012) Matching composite sketches to face photos: a component-based approach. IEEE Trans Inf Forensics Secur 8(1):191–204

    Article  Google Scholar 

  26. Härdle W, Simar L (2007) Applied multivariate statistical analysis, vol 22007. Springer, Berlin

    MATH  Google Scholar 

  27. Hassan M A, Mustafa M N, Wahba A (2017) Automatic liveness detection for facial images. In: 2017 12th International conference on computer engineering and systems (ICCES). IEEE, pp 215–220

  28. Hsu C -W, Chang C -C et al (2003) A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University

  29. Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recognit 38(12):2270–2285

    Article  Google Scholar 

  30. Jan V, Drahanskỳ M, Dvor R, Yanushkevich S N, et al. (2012) Thermal face recognition: a fusion approach. In: 2012 Third international conference on emerging security technologies. IEEE, pp 39–42

  31. Jayan T J, Aneesh R (2018) Image quality measures based face spoofing detection algorithm for online social media. In: 2018 International CET conference on control, communication, and computing (IC4). IEEE, pp 245–249

  32. Jiang C, Chen S, Zhang B, Chen Y, Bo Y, Feng Z (2018) Effectiveness analysis of the covariance matrix for spoofing detection application. In: 2018 Ubiquitous positioning, indoor navigation and location-based services (UPINLBS). IEEE, pp 1–5

  33. Jourabloo A, Liu Y, Liu X (2018) Face de-spoofing: anti-spoofing via noise modeling. In: Proceedings of the European conference on computer vision (ECCV), pp 290–306

  34. Jung H G, Kim J (2010) Constructing a pedestrian recognition system with a public open database, without the necessity of re-training: an experimental study. Pattern Anal Appl 13(2):223–233

    Article  MathSciNet  Google Scholar 

  35. Kim J K, Park H W (1999) Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans Med Imaging 18(3):231–238

    Article  Google Scholar 

  36. Kim G, Eum S, Suhr J K, Kim D I, Park K R, Kim J (2012) Face liveness detection based on texture and frequency analyses. In: 2012 5th IAPR international conference on biometrics (ICB). IEEE, pp 67–72

  37. Kim W, Suh S, Han J -J (2015) Face liveness detection from a single image via diffusion speed model. IEEE Trans Image Process 24(8):2456–2465

    Article  MathSciNet  Google Scholar 

  38. Kollreider K, Fronthaler H, Bigun J (2008) Verifying liveness by multiple experts in face biometrics. In: 2008 IEEE Computer Society conference on computer vision and pattern recognition workshops. IEEE, pp 1–6

  39. Komulainen J, Hadid A, Pietikäinen M, Anjos A, Marcel S (2013) Complementary countermeasures for detecting scenic face spoofing attacks. In: 2013 International conference on biometrics (ICB). IEEE, pp 1–7

  40. Kuncheva L I, Whitaker C J (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181–207

    Article  Google Scholar 

  41. Lagorio A, Tistarelli M, Cadoni M, Fookes C, Sridharan S (2013) Liveness detection based on 3d face shape analysis. In: 2013 International workshop on biometrics and forensics (IWBF). IEEE, pp 1–4

  42. Lai C -L, Chen J -H, Hsu J -Y, Chu C -H (2013) Spoofing face detection based on spatial and temporal features analysis. In: 2013 IEEE 2nd global conference on consumer electronics (GCCE). IEEE, pp 301–302

  43. Lakshminarayana N N, Narayan N, Napp N, Setlur S, Govindaraju V (2017) A discriminative spatio-temporal mapping of face for liveness detection. In: 2017 IEEE international conference on identity, security and behavior analysis (ISBA). IEEE, pp 1–7

  44. Leng L, Zhang J, Khan M K, Chen X, Alghathbar K (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in dct domain. Int J Phys Sci 5(17):2543–2554

    Google Scholar 

  45. Leng L, Li M, Teoh A B J (2013) Conjugate 2dpalmhash code for secure palm-print-vein verification. In: 2013 6th International congress on image and signal processing (CISP), vol 3. IEEE, pp 1705–1710

  46. Leng L, Teoh A B J, Li M, Khan M K (2014) A remote cancelable palmprint authentication protocol based on multi-directional two-dimensional palmphasor-fusion. Secur Commun Netw 7(11):1860–1871

    Article  Google Scholar 

  47. Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76(1):333–354

    Article  Google Scholar 

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

  49. Li Y, Po L -M, Xu X, Feng L, Yuan F (2016) Face liveness detection and recognition using shearlet based feature descriptors. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 874–877

  50. Liu W (2014) Face liveness detection using analysis of fourier spectra based on hair. In: 2014 International conference on wavelet analysis and pattern recognition. IEEE, pp 75–80

  51. Liu X, Lu R, Liu W (2017) Face liveness detection based on enhanced local binary patterns. In: 2017 Chinese automation congress (CAC). IEEE, pp 6301–6305

  52. Liu Y, Stehouwer J, Jourabloo A, Liu X (2019) Deep tree learning for zero-shot face anti-spoofing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4680–4689

  53. Lowe D G (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2. IEEE, pp 1150–1157

  54. Luan X, Wang H, Ou W, Liu L (2017) Face liveness detection with recaptured feature extraction. In: 2017 International conference on security, pattern analysis, and cybernetics (SPAC). IEEE, pp 429–432

  55. Määttä J, Hadid A, Pietikäinen M (2011) Face spoofing detection from single images using micro-texture analysis. In: 2011 international joint conference on biometrics (IJCB). IEEE, pp 1–7

  56. Marsland S (2015) Machine learning: An algorithmic perspective. CRC Press

  57. Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2002) A no-reference perceptual blur metric. In: Proceedings. International conference on image processing, vol 3. IEEE, pp III–III

  58. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  59. Okereafor K, Onime C, Osuagwu O (2017) Enhancing biometric liveness detection using trait randomization technique. In: 2017 UKSim-AMSS 19th international conference on computer modelling & simulation (UKSim). IEEE, pp 28–33

  60. Pan G, Sun L, Wu Z, Lao S (2007) Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: 2007 IEEE 11th international conference on computer vision. IEEE, pp 1–8

  61. Patel K, Han H, Jain A K (2016) Secure face unlock: spoof detection on smartphones. IEEE Trans Pattern Anal Mach Intell 11(10):2268–2283

    Google Scholar 

  62. Peixoto B, Michelassi C, Rocha A (2011) Face liveness detection under bad illumination conditions. In: 2011 18th IEEE international conference on image processing. IEEE, pp 3557–3560

  63. Pinto A, Schwartz W R, Pedrini H, de Rezende Rocha A (2015) Using visual rhythms for detecting video-based facial spoof attacks. IEEE Trans Inf Forensics Secur 10(5):1025–1038

    Article  Google Scholar 

  64. Ramachandra R, Busch C (2017) Presentation attack detection methods for face recognition systems: a comprehensive survey. ACM Comput Surv (CSUR) 50(1):1–37

    Article  Google Scholar 

  65. Schütze H, Manning C D, Raghavan P (2008) Introduction to information retrieval, vol 39. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  66. Schwartz W R, Rocha A, Pedrini H (2011) Face spoofing detection through partial least squares and low-level descriptors. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–8

  67. Sun L, Pan G, Wu Z, Lao S (2007) Blinking-based live face detection using conditional random fields. In: International conference on biometrics. Springer, pp 252–260

  68. Tan X, Li Y, Liu J, Jiang L (2010) Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: European conference on computer vision. Springer, pp 504–517

  69. Tronci R, Giacinto G, Roli F (2009) Dynamic score combination: a supervised and unsupervised score combination method. In: International workshop on machine learning and data mining in pattern recognition. Springer, pp 163–177

  70. Tronci R, Muntoni D, Fadda G, Pili M, Sirena N, Murgia G, Ristori M, Ricerche S, Roli F (2011) Fusion of multiple clues for photo-attack detection in face recognition systems. In: 2011 International joint conference on biometrics (IJCB). IEEE, pp 1–6

  71. Vu N -S, Caplier A (2010) Face recognition with patterns of oriented edge magnitudes. In: European conference on computer vision. Springer, pp 313–326

  72. Wang D, Hoi S C, He Y, Zhu J, Mei T, Luo J (2013) Retrieval-based face annotation by weak label regularized local coordinate coding. IEEE Trans Pattern Anal Mach Intell 36(3):550–563

    Article  Google Scholar 

  73. Waske B, Benediktsson J A (2007) Fusion of support vector machines for classification of multisensor data. IEEE Trans Geosci Remote Sens 45 (12):3858–3866

    Article  Google Scholar 

  74. Wen D, Han H, Jain A K (2015) Face spoof detection with image distortion analysis. IEEE Trans Inf Forensics Secur 10(4):746–761

    Article  Google Scholar 

  75. Wu J, Rehg J M (2010) Centrist: a visual descriptor for scene categorization. IEEE Trans Pattern Anal Mach Intell 33(8):1489–1501

    Google Scholar 

  76. Yan J, Zhang Z, Lei Z, Yi D, Li S Z (2012) Face liveness detection by exploring multiple scenic clues. In: 2012 12th International conference on control automation robotics & vision (ICARCV). IEEE, pp 188–193

  77. Yang L (2014) Face liveness detection by focusing on frontal faces and image backgrounds. In: 2014 International conference on wavelet analysis and pattern recognition. IEEE, pp 93–97

  78. Yeh C -H, Chang H -H (2017) Face liveness detection with feature discrimination between sharpness and blurriness. In: 2017 Fifteenth IAPR international conference on machine vision applications (MVA). IEEE, pp 398–401

  79. Yeh C -H, Chang H -H (2018) Face liveness detection based on perceptual image quality assessment features with multi-scale analysis. In: 2018 IEEE Winter conference on applications of computer vision (WACV). IEEE, pp 49–56

  80. Zhang Z, Yan J, Liu S, Lei Z, Yi D, Li S Z (2012) A face antispoofing database with diverse attacks. In: 2012 5th IAPR international conference on Biometrics (ICB). IEEE, pp 26–31

  81. Ziegler A, Christiansen E, Kriegman D, Belongie S J (2012) Locally uniform comparison image descriptor. In: Advances in neural information processing systems, pp 1–9

Download references

Author information

Authors and Affiliations

  1. PESIT - Bangalore South Campus, Bengaluru, Karnataka, India

    Pooja R. Patil & Subhash S. Kulkarni

Authors
  1. Pooja R. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subhash S. Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subhash S. Kulkarni.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patil, P.R., Kulkarni, S.S. Survey of non-intrusive face spoof detection methods. Multimed Tools Appl 80, 14693–14721 (2021). https://doi.org/10.1007/s11042-020-10338-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10338-1

Keywords

Search

Navigation