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Conclusions and Future Work

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Advances in Face Presentation Attack Detection

Part of the book series: Synthesis Lectures on Computer Vision ((SLCV))

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Abstract

Through the release of three large-scale datasets and the successful holding of three competitions, we have promoted the development of the face anti-spoofing community. In this chapter, we will summarize our work in recent years from two aspects of datasets and competitions, including the characteristics of datasets CASIA-SURF, CASIA-SURF CeFA and CASIA-SURF HiFiMask, the preliminary solutions of benchmark methods MS-SEF, PSMM-Net, and CCL, and the core ideas of excellent competition algorithms. In addition, based on the development trend of competition algorithms in recent years and the demand for anti-spoofing technology in daily life, we determine the future work direction, including Flexible Modal Face Anti-Spoofing, Generalizable Face Anti-spoofing and Surveillance Face Anti-spoofing aspects.

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References

  1. Zhang S, Liu A, Wan J, Liang Y, Guo G, Escalera S, Escalante HJ, Li SZ (2020) Casia-surf: a large-scale multi-modal benchmark for face anti-spoofing. TBIOM 2(2):182–193

    Google Scholar 

  2. Liu Y, Jourabloo A, Liu X (2018) Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR

    Google Scholar 

  3. Liu A, Tan Z, Wan J, Liang Y, Lei Z, Guo G, Li SZ (2021) Face anti-spoofing via adversarial cross-modality translation. IEEE Trans Inf Forensics Secur 16:2759–2772

    Google Scholar 

  4. Liu A, Liang Y (2022) Ma-vit: modality-agnostic vision transformers for face anti-spoofing. In: Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI-22, pp 1180–1186. International joint conferences on artificial intelligence organization

    Google Scholar 

  5. Liu A, Wan J, Jiang N, Wang H, Liang Y (2022) Disentangling facial pose and appearance information for face anti-spoofing. In: 2022 26th international conference on pattern recognition (ICPR), pp 4537–4543. IEEE

    Google Scholar 

  6. Boulkenafet Z (2017) A competition on generalized software based face presentation attack detection in mobile scenarios. In: IJCB

    Google Scholar 

  7. Zhang Y, Yin Z, Li Y, Yin G, Yan J, Shao J, Liu Z (2020) Celeba-spoof: large-scale face anti-spoofing dataset with rich annotations. In: ECCV

    Google Scholar 

  8. Liu A, Zhao C, Yu Z, Wan J, Su A, Liu X, Tan Z, Escalera S, **ng J, Liang Y et al (2022) Contrastive context-aware learning for 3d high-fidelity mask face presentation attack detection. IEEE Trans Inf Forensics Secur 17:2497–2507

    Google Scholar 

  9. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR

    Google Scholar 

  10. Li X, Wan J, ** Y, Liu A, Guo G, Li SZ (2020) 3dpc-net: 3d point cloud network for face anti-spoofing. In: 2020 IEEE international joint conference on biometrics (IJCB). IEEE, pp 1–8

    Google Scholar 

  11. Patel K, Han H, Jain AK (2016) Secure face unlock: spoof detection on smartphones. IEEE TIFS

    Google Scholar 

  12. George A, Marcel S (2019) Deep pixel-wise binary supervision for face presentation attack detection. In: ICB

    Google Scholar 

  13. Yu Z, Zhao C, Wang Z, Qin Y, Su Z, Li X, Zhou F, Zhao G (2020) Searching central difference convolutional networks for face anti-spoofing. In: CVPR

    Google Scholar 

  14. Zhang K-Y, Yao T, Zhang J, Tai Y, Ding S, Li J, Huang F, Song H, Ma L (2020) Face anti-spoofing via disentangled representation learning. In: ECCV

    Google Scholar 

  15. Liu Y, Stehouwer J, Liu X (2020) On disentangling spoof trace for generic face anti-spoofing. In: ECCV. Springer, pp 406–422

    Google Scholar 

  16. 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

    Google Scholar 

  17. Tu X, Zhang H, **e M, Luo Y, Zhang Y, Ma Z (2019) Deep transfer across domains for face antispoofing. J Electron Imaging 28(4):043001

    Google Scholar 

  18. Wang G, Han H, Shan S, Chen X (2019) Improving cross-database face presentation attack detection via adversarial domain adaptation. In: ICB. IEEE

    Google Scholar 

  19. Manoel Camillo O, Penna N, Koerich AL, Britto Jr AS, Israel A, Laurensi R, Menon LT (2019) Style transfer applied to face liveness detection with user-centered models. ar**v:1907.07270

  20. Yin Z, Shao J, Yang B, Zhang J (2021) Few-shot domain expansion for face anti-spoofing. ar**v:2106.14162

  21. Shao R, Lan X, Yuen PC (2020) Regularized fine-grained meta face anti-spoofing. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 11974–11981

    Google Scholar 

  22. Qin Y, Yu Z, Yan L, Wang Z, Zhao C, Lei Z (2021) Meta-teacher for face anti-spoofing. IEEE Trans Pattern Anal Mach Intell

    Google Scholar 

  23. Chen Z, Yao T, Sheng K, Ding S, Tai Y, Li J, Huang F, ** X (2021) Generalizable representation learning for mixture domain face anti-spoofing. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 1132–1139

    Google Scholar 

  24. Wang J, Zhang J, Bian Y, Cai Y, Wang C, Pu S (2021) Self-domain adaptation for face anti-spoofing. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp 2746–2754

    Google Scholar 

  25. Zhou Q, Zhang K-Y, Yao T, Yi R, Ding S, Ma L (2022) Adaptive mixture of experts learning for generalizable face anti-spoofing. In: Proceedings of the 30th ACM international conference on multimedia, pp 6009–6018

    Google Scholar 

  26. George A, Marcel S (2021) On the effectiveness of vision transformers for zero-shot face anti-spoofing

    Google Scholar 

  27. Huang H-P, Sun D, Liu Y, Chu W-S, **ao T, Yuan J, Adam H, Yang M-H (2022) Adaptive transformers for robust few-shot cross-domain face anti-spoofing. ar**v preprint ar**v:2203.12175

  28. Cheng Z, Zhu X, Gong S (2018) Surveillance face recognition challenge. ar**v preprint ar**v:1804.09691

  29. Nada H, Sindagi VA, Zhang H, Patel VM (2018) Pushing the limits of unconstrained face detection: a challenge dataset and baseline results. In: 2018 IEEE 9th international conference on biometrics theory, applications and systems (BTAS), pp 1–10. IEEE

    Google Scholar 

  30. Grgic M, Delac K, Grgic S (2011) Scface-surveillance cameras face database. Multimedia Tools Appl 51(3):863–879

    Article  Google Scholar 

  31. Kim M, Jain AK, Liu X (2022) Adaface: quality adaptive margin for face recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), June 2022, pp 18750–18759

    Google Scholar 

  32. Li P, Prieto L, Mery D, Flynn PJ (2019) On low-resolution face recognition in the wild: comparisons and new techniques. IEEE Trans Inf Forensics Secur 14(8):2000–2012

    Article  Google Scholar 

  33. Zhong Y, Deng W, Hu J, Zhao D, Li X, Wen D (2021) Sface: sigmoid-constrained hypersphere loss for robust face recognition. IEEE Trans Image Process 30:2587–2598

    Article  Google Scholar 

  34. Erdogmus N, Marcel S (2013) Spoofing in 2d face recognition with 3d masks and anti-spoofing with kinect. In: IEEE 6th international conference on biometrics: theory, applications and systems (BTAS’13), pp 1–8

    Google Scholar 

  35. Galbally J, Satta R (2016) Three-dimensional and two-and-a-half-dimensional face recognition spoofing using three-dimensional printed models. IET Biometrics

    Google Scholar 

  36. Steiner H, Kolb A, Jung N (2016) Reliable face anti-spoofing using multispectral swir imaging. In: ICB. IEEE

    Google Scholar 

  37. Jia S, Guo G, Xu Z (2020) A survey on 3d mask presentation attack detection and countermeasures. Pattern Recognit

    Google Scholar 

  38. Liu S-Q, Lan X, Yuen PC (2018) Remote photoplethysmography correspondence feature for 3d mask face presentation attack detection. In: ECCV

    Google Scholar 

  39. Fang H, Liu A, Wan J, Escalera S, Zhao C, Zhang X, Li SZ, Lei Z (2023) Surveillance face anti-spoofing. ar**v preprint ar**v:2301.00975

  40. Chen X, Xu S, Ji Q, Cao S (2021) A dataset and benchmark towards multi-modal face anti-spoofing under surveillance scenarios. IEEE Access 9:28140–28155

    Article  Google Scholar 

  41. George A, Mostaani Z, Geissenbuhler D, Nikisins O, Anjos A, Marcel S (2019) Biometric face presentation attack detection with multi-channel convolutional neural network. TIFS

    Google Scholar 

  42. Liu Y, Stehouwer J, Jourabloo A, Liu X (2019) Deep tree learning for zero-shot face anti-spoofing. In: CVPR

    Google Scholar 

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Author information

Authors and Affiliations

  1. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Jun Wan

  2. Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, China

    Guodong Guo

  3. Department of Mathematics and Informatics, University of Barcelona and Computer Vision Center, Barcelona, Spain

    Sergio Escalera

  4. Department of Computer Science, Instituto Nacional de Astrofísica, Optica y Electrónica, Puebla, Mexico

    Hugo Jair Escalante

  5. AI Lab, Westlake University, Hangzhou, China

    Stan Z. Li

Authors
  1. Jun Wan
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  2. Guodong Guo
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  3. Sergio Escalera
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  4. Hugo Jair Escalante
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  5. Stan Z. Li
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Corresponding author

Correspondence to Jun Wan .

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Wan, J., Guo, G., Escalera, S., Escalante, H.J., Li, S.Z. (2023). Conclusions and Future Work. In: Advances in Face Presentation Attack Detection. Synthesis Lectures on Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-031-32906-7_5

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