Skip to main content
SpringerLink
Log in

A robust framework for spoofing detection in faces using deep learning

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Face recognition is used in biometric systems to verify and authenticate an individual. However, most face authentication systems are prone to spoofing attacks such as replay attacks, attacks using 3D masks etc. Thus, the importance of face anti-spoofing algorithms is becoming essential in these systems. Recently, deep learning has emerged and achieved excellent results in challenging tasks related to computer vision. The proposed framework relies on the extraction of features from the faces of individuals. The approach relies on dimensionality reduction and feature extraction of input frames using pre-trained weights of convolutional autoencoders, followed by classification using softmax classifier. Experimental analysis on three benchmarks, Idiap Replay Attack, CASIA- FASD and 3DMAD, shows that the proposed framework can attain results comparable to state-of-the-art methods in both cross-database and intra-database testing.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Alotaibi, A., Mahmood, A.: Deep face liveness detection based on nonlinear diffusion using convolution neural network. SIViP 11(4), 713–720 (2017)

    Article  Google Scholar 

  2. Menotti, D., Chiachia, G., Pinto, A., Schwartz, W., Pedrini, H., Falco, A., Rocha, A.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)

    Article  Google Scholar 

  3. Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 762–777 (2015)

    Article  Google Scholar 

  4. Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access 2(1), 1530–1552 (2014)

    Article  Google Scholar 

  5. Feng, L., Po, L., Li, Y., Xu, X., Yuan, F., Cheung, T., Cheung, K.: Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J. Vis. Commun. Image Represent. 38(C), 451–460 (2016)

    Article  Google Scholar 

  6. Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.: A face antispoofing database with diverse attacks. International Conference on Biometrics 26–31, (2012)

  7. Erdogmus N., Marcel S.: Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, 1–6 (2013)

  8. Li, L., Correia, P., Hadid, A.: Face recognition under spoofing attacks: countermeasures and research directions. IET Biom 7(1), 3–14 (2017)

    Article  Google Scholar 

  9. Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: An investigation of local descriptors for biometric spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 849–863 (2015)

    Article  Google Scholar 

  10. Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. Biometric technology for human identification. SPIE 5404(1), 296–304 (2004)

    Google Scholar 

  11. Mahitha, M.: Face spoof detection using machine learning with colour features. Int. Res. J. Eng. Technol. 9(5), 1–4 (2018)

    Google Scholar 

  12. Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision - ECCV, : Lecture notes in computer science 6316, pp. 504–517. Springer, Heidelberg (2010)

    Google Scholar 

  13. Bharadwaj, S., Dhamecha, I., Vatsa, M., Singh, R.: Face antispoofing via motion magnification and multifeature videolet aggregation. Indraprastha Inst. Inf. Technol., New Delhi, India IIITD-TR2014-002(002) 1-14 (2015)

  14. Tammina, S.: Transfer learning using vgg-16 with deep convolutional neural network for classifying images. Int. J. Sci. Res. Pub. (IJSRP) 9(10), 9420 (2019)

    Google Scholar 

  15. Tu, X., Fang, Y.: Ultra-deep neural network for face anti-spoofing. Neural Inf. Process. 10635(1), 686–695 (2017)

    Google Scholar 

  16. Zinelabidine, B., Akhtar, Z., Feng, X., Hadid, A.: Analysis of textural features for face biometric anti-spoofing. Face Anti-spoofing in Biometric Systems 1–5, (2017)

  17. Haoliang, L., Peisong, H., Wang, S., Rocha, A., Jiang, X., Kot, A.: Learning generalized deep feature representation for face anti-spoofing. IEEE Trans. Inf. Forensics Secur. 13(4), 2639–2652 (2016)

    Google Scholar 

  18. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  19. Chen, H., Hu, G., Lei, Z., Chen, Y., Robertson, N., Li, S.: Attention-based two-stream convolutional networks for face spoofing detection. IEEE Trans. Inf. Forensics Secur. 15(1), 578–593 (2019)

    Google Scholar 

  20. Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. Lect. Notes Comput. Sci. 4642(1), 252–260 (2007)

    Article  Google Scholar 

  21. Khammari, M.: Robust face anti-spoofing using CNN with LBP and WLD. IET Image Proc. 13(11), 1880–1884 (2019)

    Article  Google Scholar 

  22. Alrikabi, W., Alnasrallah, A., Alrikabi, A.: System designing for face spoofing detection using convolution neural network (cnn). Solid State Technol. 63(6), 14646–14655 (2020)

    Google Scholar 

  23. Sun, Y., Xiong, H., Yiu S.: Understanding deep face anti-spoofing: from the perspective of data. Vis Comput, 1–15 (2020)

  24. Atoum Y., Liu Y., Jourabloo A., Liu X.: Face anti-spoofing using patch and depth-based cnns. Proc. IEEE Int. Joint Conf. Biometrics(IJCB), 319–328 (2017)

  25. Li, L., Xia, Z.: 3d face mask presentation attack detection based on intrinsic image analysis. arXiv:1903.11303v1 [cs.CV], 1–24 (2019)

  26. Pereira, T., Komulainen, J., Anjos, A., Martino, J., Hadid, A., Pietikainen, M., Marcel, S.: Face liveness detection using dynamic texture. EURASIP J. Image Video Process. 2014(2), 1–15 (2014)

    Google Scholar 

  27. Yang, J., Lei, Z., Liao, S., Li, S.: Face liveness detection with component dependent descriptor. International Conference on Biometrics 85–100, (2013)

  28. Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. Adv. Biom. 4642, 252–260 (2007)

    Article  Google Scholar 

  29. Liu, S., Yuen, P., Zhao, S.: 3d mask face anti-spoofing with remote photoplethysmography. Proc. ECCV 9911(1), 85–100 (2016)

    Google Scholar 

  30. Pereira, T., Anjos, A., Marcel, S.: Lbptop based counter-measure against face spoofing attacks. ACCV 2012: Comput. Vis. - ACCV 2012 Workshop 7728, 121–132 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  32. Rehman, Y., Po, L., Liu, M.: Livenet: improving features generalization for face liveness detection using convolution neural networks. Expert Syst. Appl. 108, 159–169 (2018)

    Article  Google Scholar 

  33. Gan, J., Li, S., Zhai, Y.,Liu, C.: 3d convolutional neural network based on face anti-spoofing. 2nd International Conference on Multimedia and Image Processing (ICMIP), 252–260 (2017)

  34. Wang, G., Han, H., Shan, S., Chen, X.: Improving cross-database face presentation attack detection via adversarial domain adaptation. International Conference on Biometrics 1–8, (2019)

  35. Sun, W., Song, Y., Zhao, H., Jin, Z.: A face spoofing detection method based on domain adaptation and lossless size adaptation. IEEE Access 8, 66553–66563 (2020)

    Article  Google Scholar 

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

  37. Matta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using micro-texture analysis. International Joint Conference on Biometrics (IJCB) 1–7, (2011)

  38. Li, X., Komulainen, J.: Generalized face anti-spoofing by detecting pulse from face videos. International Conference on Pattern Recognition 4244–4249 (2017)

  39. Galbally, Marcel S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014)

    Article  MathSciNet  Google Scholar 

  40. Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.: A face antispoofing database with diverse attacks. International Conference on Biometrics 26–31 (2012)

Download references

Author information

Authors and Affiliations

  1. Division of Computer Engineering, Netaji Subhas Institute of Technology, Delhi, India

    Shefali Arora, M. P. S. Bhatia & Vipul Mittal

Authors
  1. Shefali Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. P. S. Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vipul Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shefali Arora.

Ethics declarations

Funding

The authors have not received any funding for this research

Conflict of interest

The authors Shefali Arora, M.P.S Bhatia and Vipul Mittal declare that they have no conflict of interest.

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

Arora, S., Bhatia, M.P.S. & Mittal, V. A robust framework for spoofing detection in faces using deep learning. Vis Comput 38, 2461–2472 (2022). https://doi.org/10.1007/s00371-021-02123-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02123-4

Keywords

Search

Navigation