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A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing (2306.14313v1)

Published 25 Jun 2023 in cs.CV and cs.LG

Abstract: Face anti-spoofing (FAS) is indispensable for a face recognition system. Many texture-driven countermeasures were developed against presentation attacks (PAs), but the performance against unseen domains or unseen spoofing types is still unsatisfactory. Instead of exhaustively collecting all the spoofing variations and making binary decisions of live/spoof, we offer a new perspective on the FAS task to distinguish between normal and abnormal movements of live and spoof presentations. We propose Geometry-Aware Interaction Network (GAIN), which exploits dense facial landmarks with spatio-temporal graph convolutional network (ST-GCN) to establish a more interpretable and modularized FAS model. Additionally, with our cross-attention feature interaction mechanism, GAIN can be easily integrated with other existing methods to significantly boost performance. Our approach achieves state-of-the-art performance in the standard intra- and cross-dataset evaluations. Moreover, our model outperforms state-of-the-art methods by a large margin in the cross-dataset cross-type protocol on CASIA-SURF 3DMask (+10.26% higher AUC score), exhibiting strong robustness against domain shifts and unseen spoofing types.

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References (55)
  1. Face anti-spoofing using patch and depth-based cnns. In 2017 IEEE International Joint Conference on Biometrics (IJCB), pages 319–328. IEEE, 2017.
  2. Oulu-npu: A mobile face presentation attack database with real-world variations. In 2017 12th IEEE international conference on automatic face & gesture recognition (FG 2017), pages 612–618. IEEE, 2017.
  3. How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3d facial landmarks). In Proceedings of the IEEE International Conference on Computer Vision, pages 1021–1030, 2017.
  4. Learning meta pattern for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 17:1201–1213, 2022.
  5. Attention-based two-stream convolutional networks for face spoofing detection. IEEE Transactions on Information Forensics and Security, 15:578–593, 2019.
  6. Channel-wise topology refinement graph convolution for skeleton-based action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13359–13368, 2021.
  7. 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), pages 1–7. IEEE, 2012.
  8. Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5203–5212, 2020.
  9. Learnable multi-level frequency decomposition and hierarchical attention mechanism for generalized face presentation attack detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3722–3731, 2022.
  10. Towards fast, accurate and stable 3d dense face alignment. In European Conference on Computer Vision, pages 152–168. Springer, 2020.
  11. Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 6546–6555, 2018.
  12. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  13. Multi-teacher single-student visual transformer with multi-level attention for face spoofing detection. In BMVC, 2021.
  14. Single-side domain generalization for face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8484–8493, 2020.
  15. A new representation of skeleton sequences for 3d action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3288–3297, 2017.
  16. Skeleton-based action recognition with convolutional neural networks. In 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pages 597–600. IEEE, 2017.
  17. Unsupervised domain adaptation for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 13(7):1794–1809, 2018.
  18. Ce Liu et al. Beyond pixels: exploring new representations and applications for motion analysis. PhD thesis, Massachusetts Institute of Technology, 2009.
  19. Spatio-temporal lstm with trust gates for 3d human action recognition. In European conference on computer vision, pages 816–833. Springer, 2016.
  20. Adaptive normalized representation learning for generalizable face anti-spoofing. In Proceedings of the 29th ACM International Conference on Multimedia, 2021.
  21. Dual reweighting domain generalization for face presentation attack detection. arXiv preprint arXiv:2106.16128, 2021.
  22. Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 389–398, 2018.
  23. Deep tree learning for zero-shot face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4680–4689, 2019.
  24. Disentangling and unifying graph convolutions for skeleton-based action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 143–152, 2020.
  25. Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10772–10781, 2019.
  26. Domain agnostic feature learning for image and video based face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 802–803, 2020.
  27. Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1010–1019, 2016.
  28. Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10023–10031, 2019.
  29. Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12026–12035, 2019.
  30. Interpretable 3d human action analysis with temporal convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 20–28, 2017.
  31. Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.
  32. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  33. 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, pages 20281–20290, 2022.
  34. Improving cross-database face presentation attack detection via adversarial domain adaptation. In 2019 International Conference on Biometrics (ICB), pages 1–8. IEEE, 2019.
  35. Vlad-vsa: Cross-domain face presentation attack detection with vocabulary separation and adaptation. In Proceedings of the 29th ACM International Conference on Multimedia, pages 1497–1506, 2021.
  36. Domain generalization via shuffled style assembly for face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4123–4133, 2022.
  37. Multi-perspective features learning for face anti-spoofing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4116–4122, 2021.
  38. Deep spatial gradient and temporal depth learning for face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5042–5051, 2020.
  39. Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, 10(4):746–761, 2015.
  40. Fake it till you make it: face analysis in the wild using synthetic data alone. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3681–3691, 2021.
  41. 3d face reconstruction with dense landmarks, 2022.
  42. Exploiting non-uniform inherent cues to improve presentation attack detection. In 2021 IEEE International Joint Conference on Biometrics (IJCB), pages 1–8. IEEE, 2021.
  43. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-second AAAI conference on artificial intelligence, 2018.
  44. Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601, 2014.
  45. Face anti-spoofing: Model matters, so does data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3507–3516, 2019.
  46. Dual-cross central difference network for face anti-spoofing. arXiv preprint arXiv:2105.01290, 2021.
  47. Nas-fas: Static-dynamic central difference network search for face anti-spoofing. IEEE transactions on pattern analysis and machine intelligence, 43(9):3005–3023, 2020.
  48. Searching central difference convolutional networks for face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5295–5305, 2020.
  49. Face anti-spoofing via disentangled representation learning. In European Conference on Computer Vision, pages 641–657. Springer, 2020.
  50. On geometric features for skeleton-based action recognition using multilayer lstm networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 148–157. IEEE, 2017.
  51. A face antispoofing database with diverse attacks. In 2012 5th IAPR international conference on Biometrics (ICB), pages 26–31. IEEE, 2012.
  52. Selective domain-invariant feature alignment network for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 16:5352–5365, 2021.
  53. Adaptive mixture of experts learning for generalizable face anti-spoofing. arXiv preprint arXiv:2207.09868, 2022.
  54. Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. In Proceedings of the AAAI conference on artificial intelligence, 2016.
  55. Face alignment in full pose range: A 3d total solution. IEEE transactions on pattern analysis and machine intelligence, 41(1):78–92, 2017.
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