Skip to main content
SpringerLink
Log in

Fully supervised contrastive learning in latent space for face presentation attack detection

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The vulnerability of conventional face recognition systems to face presentation or face spoofing attacks has attracted a great deal of attention from information security, forensic, and biometric communities during the past few years. With the recent advancement and availability of cutting-edge computing technologies, sophisticated and computationally expensive solutions to many problems have been made possible. Accordingly, deep learning-based face presentation attack detection (PAD) methods have gained increasing popularity. In this research, we propose a supervised contrastive learning approach to tackle the face anti-spoofing problem. Essentially, the latent space encoding is achieved through an encoder network using the contrastive loss function infused with the class label information. The proposed robust encoding is followed by a simple classifier to distinguish between a real and a spoof face. To the best of our knowledge, this is the first work that uses fully supervised contrastive learning for the two-dimensional (2D) face PAD task. The performance of the proposed method is evaluated on several face anti-spoofing datasets and the results clearly show the efficacy of the proposed approach compared to other contemporary methods.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The datasets used in this study are publicly available and the relevant references have been included in the paper. The authors do not own any of the datasets used in this study.

References

  1. Parmar DN, Mehta BB (2014) Face recognition methods & applications. arXiv preprint arXiv:1403.0485

  2. Kamgar-Parsi B, Lawson W, Kamgar-Parsi B (2011) Toward development of a face recognition system for watchlist surveillance. IEEE Trans Pattern Anal Mach Intell 33(10):1925–1937

    Article  Google Scholar 

  3. Lukas S, Mitra AR, Desanti RI, Krisnadi D (2016) Student attendance system in classroom using face recognition technique. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC) pp. 1032–1035. IEEE

  4. Fayyoumi, A., Zarrad, A.: Novel solution based on face recognition to address identity theft and cheating in online examination systems. Advances in Internet of Things 2014 (2014)

  5. Patel K, Han H, Jain AK (2016) Secure face unlock: Spoof detection on smartphones. IEEE Trans Inf Forensics Secur 11(10):2268–2283

    Article  Google Scholar 

  6. Lee H, Park S-H, Yoo J-H, Jung S-H, Huh J-H (2020) Face recognition at a distance for a stand-alone access control system. Sensors 20(3):785

    Article  Google Scholar 

  7. Grother PJ, Ngan ML, Hanaoka KK, et al (2018) Ongoing face recognition vendor test (frvt) part 2: Identification

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

    Article  Google Scholar 

  9. Anjos A, Chakka MM, Marcel S (2014) Motion-based counter-measures to photo attacks in face recognition. IET biometrics 3(3):147–158

    Article  Google Scholar 

  10. Patel K, Han H, Jain AK (2016) Cross-database face antispoofing with robust feature representation. In: Chinese Conference on Biometric Recognition pp. 611–619. Springer

  11. Shao R, Lan X, Yuen PC (2017) Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3d mask face anti-spoofing. In: 2017 IEEE International Joint Conference on Biometrics (IJCB) pp. 748–755. IEEE

  12. Sun W, Song Y, Chen C, Huang J, Kot AC (2020) Face spoofing detection based on local ternary label supervision in fully convolutional networks. IEEE Trans Inf Forensics Secur 15:3181–3196

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. George A, Marcel S (2020) Learning one class representations for face presentation attack detection using multi-channel convolutional neural networks. IEEE Trans Inf Forensics Secur 16:361–375

    Article  Google Scholar 

  15. Rehman YAU, Po L-M, Liu M, Zou Z, Ou W, Zhao Y (2019) Face liveness detection using convolutional-features fusion of real and deep network generated face images. J Vis Commun Image Represent 59:574–582

    Article  Google Scholar 

  16. Shao R, Lan X, Yuen PC (2018) Joint discriminative learning of deep dynamic textures for 3d mask face anti-spoofing. IEEE Trans Inf Forensics Secur 14(4):923–938

    Article  Google Scholar 

  17. Rehman YAU, Po LM, Liu M (2020) Slnet: Stereo face liveness detection via dynamic disparity-maps and convolutional neural network. Expert Syst Appl 142, 113002

  18. Kotwal K, Bhattacharjee S, Marcel S (2019) Multispectral deep embeddings as a countermeasure to custom silicone mask presentation attacks. IEEE Transactions on Biometrics, Behavior, and Identity Science 1(4):238–251

    Article  Google Scholar 

  19. Heusch G, George A, Geissbühler D, Mostaani Z, Marcel S (2020) Deep models and shortwave infrared information to detect face presentation attacks. IEEE Transactions on Biometrics, Behavior, and Identity Science 2(4):399–409

    Article  Google Scholar 

  20. George A, Mostaani Z, Geissenbuhler D, Nikisins O, Anjos A, Marcel S (2019) Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Trans Inf Forensics Secur 15:42–55

    Article  Google Scholar 

  21. Chen H, Chen Y, Tian X, Jiang R (2019) A cascade face spoofing detector based on face anti-spoofing r-cnn and improved retinex lbp. IEEE Access 7:170116–170133

    Article  Google Scholar 

  22. Bousnina N, Zheng L, Mikram M, Ghouzali S, Minaoui K (2021) Unraveling robustness of deep face anti-spoofing models against pixel attacks. Multimedia Tools and Applications 80(5):7229–7246

    Article  Google Scholar 

  23. Fatemifar, S, Arashloo SR, Awais M, Kittler J (2021) Client-specific anomaly detection for face presentation attack detection. Pattern Recognit 112, 107696

  24. Wang G, Han H, Shan S, Chen X (2020) Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection. IEEE Trans Inf Forensics Secur 16:56–69

    Article  Google Scholar 

  25. Fatemifar S, Asadi S, Awais M, Akbari A, Kittler J (2022) Face spoofing detection ensemble via multistage optimisation and pruning. Pattern Recogn Lett 158:1–8

    Article  Google Scholar 

  26. Fatemifar S, Awais M, Akbari A, Kittler J (2021) Particle swarm and pattern search optimisation of an ensemble of face anomaly detectors. In: 2021 IEEE International Conference on Image Processing (ICIP) pp. 3622–3626. IEEE

  27. Arashloo SR (2021) Matrix-regularized one-class multiple kernel learning for unseen face presentation attack detection. IEEE Trans Inf Forensics Secur 16:4635–4647. https://doi.org/10.1109/TIFS.2021.3111766

    Article  Google Scholar 

  28. Wang Z, Wang Z, Yu Z, Deng W, Li J, Gao T, Wang Z (2022) Domain generalization via shuffled style assembly for face anti-spoofing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 4123–4133

  29. Wang CY, Lu YD, Yang ST, Lai SH (2022) Patchnet: A simple face anti-spoofing framework via fine-grained patch recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 20281–20290

  30. Hao H, Pei M, Zhao M (2019) Face liveness detection based on client identity using siamese network. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV) pp. 172–180. Springer

  31. Li L, Xia Z, Jiang X, Roli F, Feng X (2020) Compactnet: learning a compact space for face presentation attack detection. Neurocomputing 409:191–207

    Article  Google Scholar 

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

  33. Muhammad U, Yu Z, Komulainen J (2022) Self-supervised 2d face presentation attack detection via temporal sequence sampling. Pattern Recognit Lett

  34. Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. Adv Neural Inf Process Syst 33:18661–18673

    Google Scholar 

  35. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

    Article  MathSciNet  Google Scholar 

  36. Li H, Li W, Cao H, Wang S, Huang F, Kot AC (2018) Unsupervised domain adaptation for face anti-spoofing. IEEE Trans Inf Forensics Secur 13(7):1794–1809

    Article  Google Scholar 

  37. Hajiaboli MR, Ahmad MO, Wang C (2011) An edge-adapting laplacian kernel for nonlinear diffusion filters. IEEE Trans Image Process 21(4):1561–1572

    Article  MathSciNet  MATH  Google Scholar 

  38. Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 113–123

  39. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European Conference on Computer Vision pp. 630–645. Springer

  40. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 4510–4520

  41. Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning pp. 1597–1607. PMLR

  42. Grill J-B, Strub F, Altché F, Tallec C, Richemond P, Buchatskaya E, Doersch C, Avila Pires B, Guo Z, Gheshlaghi Azar M et al (2020) Bootstrap your own latent-a new approach to self-supervised learning. Adv Neural Inf Process Syst 33:21271–21284

    Google Scholar 

  43. Elsayed G, Krishnan D, Mobahi H, Regan K, Bengio S (2018) Large margin deep networks for classification. Adv Neural Inf Process Syst 31

  44. 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) pp. 1–7. IEEE

  45. Costa-Pazo A, Bhattacharjee S, Vazquez-Fernandez E, Marcel S (2016) The replay-mobile face presentation-attack database. In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG) pp. 1–7. IEEE

  46. Koshy R, Mahmood A (2020) Enhanced deep learning architectures for face liveness detection for static and video sequences. Entropy 22(10):1186

    Article  Google Scholar 

  47. Fatemifar S, Awais M, Arashloo SR, Kittler J (2019) Combining multiple one-class classifiers for anomaly based face spoofing attack detection. In: 2019 International Conference on Biometrics (ICB) pp. 1–7. IEEE

  48. Fatemifar S, Awais M, Akbari A, Kittler J (2022) Developing a generic framework for anomaly detection. Pattern Recognit 124, 108500

  49. Li H, He P, Wang S, Rocha A, Jiang X, Kot AC (2018) Learning generalized deep feature representation for face anti-spoofing. IEEE Trans Inf Forensics Secur 13(10):2639–2652

    Article  Google Scholar 

Download references

Acknowledgements

This research work was funded by the Institutional Fund Projects under grant no (IFPRC-044-611-2020). Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdul Aziz University, Jeddah, Saudi Arabia.

Author information

Authors and Affiliations

  1. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Madini O. Alassafi, Rayed AlGhamdi, Reem Alotaibi, Faris A. Kateb & Hadi Mohsen Oqaibi

  2. College of Electrical Engineering, Zhejiang University, Hangzhou, China

    Muhammad Sohail Ibrahim

  3. Research and Development, Love For Data, Karachi, Pakistan

    Imran Naseem

  4. School of Electrical, Electronic, and Computer Engineering, The University of Western Australia, Perth, Australia

    Imran Naseem

  5. College of Engineering, Karachi Institute of Economics and Technology, Karachi, Pakistan

    Imran Naseem

  6. College of Computer Science and Engineering, Department of Information Systems and Technology, University of Jeddah, Jeddah, Saudi Arabia

    Abdulrahman A. Alshdadi

  7. Research and Innovation Department, Intelexica Pvt. Ltd., Southampton, United Kingdom

    Syed Adnan Yusuf

Authors
  1. Madini O. Alassafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Sohail Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Imran Naseem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rayed AlGhamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Reem Alotaibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Faris A. Kateb
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hadi Mohsen Oqaibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Abdulrahman A. Alshdadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Syed Adnan Yusuf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Sohail Ibrahim.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alassafi, M.O., Ibrahim, M.S., Naseem, I. et al. Fully supervised contrastive learning in latent space for face presentation attack detection. Appl Intell 53, 21770–21787 (2023). https://doi.org/10.1007/s10489-023-04619-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-04619-z

Keywords

Search

Navigation