Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing (2404.14406v1)

Published 22 Apr 2024 in cs.CV

Abstract: Face recognition technology has become an integral part of modern security systems and user authentication processes. However, these systems are vulnerable to spoofing attacks and can easily be circumvented. Most prior research in face anti-spoofing (FAS) approaches it as a two-class classification task where models are trained on real samples and known spoof attacks and tested for detection performance on unknown spoof attacks. However, in practice, FAS should be treated as a one-class classification task where, while training, one cannot assume any knowledge regarding the spoof samples a priori. In this paper, we reformulate the face anti-spoofing task from a one-class perspective and propose a novel hyperbolic one-class classification framework. To train our network, we use a pseudo-negative class sampled from the Gaussian distribution with a weighted running mean and propose two novel loss functions: (1) Hyp-PC: Hyperbolic Pairwise Confusion loss, and (2) Hyp-CE: Hyperbolic Cross Entropy loss, which operate in the hyperbolic space. Additionally, we employ Euclidean feature clipping and gradient clipping to stabilize the training in the hyperbolic space. To the best of our knowledge, this is the first work extending hyperbolic embeddings for face anti-spoofing in a one-class manner. With extensive experiments on five benchmark datasets: Rose-Youtu, MSU-MFSD, CASIA-MFSD, Idiap Replay-Attack, and OULU-NPU, we demonstrate that our method significantly outperforms the state-of-the-art, achieving better spoof detection performance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (86)
  1. Liveness detection using gaze collinearity. In 2012 Third International Conference on Emerging Security Technologies, pages 62–65. IEEE, 2012.
  2. An anomaly detection approach to face spoofing detection: A new formulation and evaluation protocol. IEEE access, 5:13868–13882, 2017.
  3. Cnn based spatio-temporal feature extraction for face anti-spoofing. In 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pages 234–238. IEEE, 2017.
  4. Hyperbolic image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4453–4462, 2022.
  5. Face anti-spoofing using patch and depth-based cnns. In 2017 IEEE International Joint Conference on Biometrics (IJCB), pages 319–328. IEEE, 2017.
  6. A liveness detection method for face recognition based on optical flow field. In 2009 International Conference on Image Analysis and Signal Processing, pages 233–236. IEEE, 2009.
  7. Anomaly detection-based unknown face presentation attack detection. In 2020 IEEE International Joint Conference on Biometrics (IJCB), pages 1–9. IEEE, 2020.
  8. Face anti-spoofing based on color texture analysis. In 2015 IEEE international conference on image processing (ICIP), pages 2636–2640. IEEE, 2015.
  9. 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.
  10. Hyperbolic graph convolutional neural networks. Advances in neural information processing systems, 32, 2019.
  11. 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.
  12. Lbp- top based countermeasure against face spoofing attacks. In Computer Vision-ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012, Revised Selected Papers, Part I 11, pages 121–132. Springer, 2013.
  13. Attention-aware dual-stream network for multimodal face anti-spoofing. IEEE Transactions on Information Forensics and Security, 2023.
  14. Hyperbolic vision transformers: Combining improvements in metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7409–7419, 2022.
  15. Spoofing attack detection by anomaly detection. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8464–8468. IEEE, 2019.
  16. Combining multiple one-class classifiers for anomaly based face spoofing attack detection. In 2019 International Conference on Biometrics (ICB), pages 1–7. IEEE, 2019.
  17. Hyperbolic neural networks. Advances in neural information processing systems, 31, 2018.
  18. Curvature generation in curved spaces for few-shot learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8691–8700, 2021.
  19. A. George and S. Marcel. Deep pixel-wise binary supervision for face presentation attack detection. In 2019 International Conference on Biometrics (ICB), pages 1–8. IEEE, 2019.
  20. A. George and S. Marcel. Learning one class representations for face presentation attack detection using multi-channel convolutional neural networks. IEEE Transactions on Information Forensics and Security, 16:361–375, 2020.
  21. Drocc: Deep robust one-class classification. In International conference on machine learning, pages 3711–3721. PMLR, 2020.
  22. Clipped hyperbolic classifiers are super-hyperbolic classifiers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11–20, 2022.
  23. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  24. Time analysis of pulse-based face anti-spoofing in visible and nir. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2018.
  25. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.
  26. Gaussian mixture pdf in one-class classification: computing and utilizing confidence values. In 18th International Conference on Pattern Recognition (ICPR’06), volume 2, pages 577–580. IEEE, 2006.
  27. Liveness detection for embedded face recognition system. International Journal of Biological and Medical Sciences, 1(4):235–238, 2006.
  28. 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.
  29. Kepeng Qiu. SVDD. ”https://github.com/iqiukp/SVDD-Python”, 2020.
  30. Hyperbolic image embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6418–6428, 2020.
  31. Basn: Enriching feature representation using bipartite auxiliary supervisions for face anti-spoofing. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0–0, 2019.
  32. Context based face anti-spoofing. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 1–8. IEEE, 2013.
  33. Multi-modal multi-layer fusion network with average binary center loss for face anti-spoofing. In Proceedings of the 27th ACM International Conference on Multimedia, pages 48–56, 2019.
  34. Lorentzian distance learning for hyperbolic representations. In International Conference on Machine Learning, pages 3672–3681. PMLR, 2019.
  35. Unsupervised domain adaptation for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 13(7):1794–1809, 2018.
  36. Generalized face anti-spoofing by detecting pulse from face videos. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 4244–4249. IEEE, 2016.
  37. One-class knowledge distillation for face presentation attack detection. IEEE Transactions on Information Forensics and Security, 17:2137–2150, 2022.
  38. Face anti-spoofing via adversarial cross-modality translation. IEEE Transactions on Information Forensics and Security, 16:2759–2772, 2021.
  39. Hyperbolic visual embedding learning for zero-shot recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9273–9281, 2020.
  40. 3d mask face anti-spoofing with remote photoplethysmography. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14, pages 85–100. Springer, 2016.
  41. Adaptive normalized representation learning for generalizable face anti-spoofing. In Proceedings of the 29th ACM international conference on multimedia, pages 1469–1477, 2021.
  42. Data-fusion-based two-stage cascade framework for multimodality face anti-spoofing. IEEE Transactions on Cognitive and Developmental Systems, 14(2):672–683, 2021.
  43. 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.
  44. 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.
  45. Searching for actions on the hyperbole. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1141–1150, 2020.
  46. Continuous hierarchical representations with poincaré variational auto-encoders. Advances in neural information processing systems, 32, 2019.
  47. Mahalanobis-based one-class classification. In 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6. IEEE, 2014.
  48. M. Nickel and D. Kiela. Poincaré embeddings for learning hierarchical representations. Advances in neural information processing systems, 30, 2017.
  49. On effectiveness of anomaly detection approaches against unseen presentation attacks in face anti-spoofing. In 2018 International Conference on Biometrics (ICB), pages 75–81. IEEE, 2018.
  50. P. Oza and V. M. Patel. One-class convolutional neural network. IEEE Signal Processing Letters, 26(2):277–281, 2018.
  51. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In 2007 IEEE 11th international conference on computer vision, pages 1–8. IEEE, 2007.
  52. Deep face recognition. In BMVC 2015-Proceedings of the British Machine Vision Conference 2015. British Machine Vision Association, 2015.
  53. A. Parkin and O. Grinchuk. Recognizing multi-modal face spoofing with face recognition networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0–0, 2019.
  54. Secure face unlock: Spoof detection on smartphones. IEEE transactions on information forensics and security, 11(10):2268–2283, 2016.
  55. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.
  56. Deep anomaly detection for generalized face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019.
  57. Progressive transfer learning for face anti-spoofing. IEEE Transactions on Image Processing, 30:3946–3955, 2021.
  58. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  59. R. Sarkar. Low distortion delaunay embedding of trees in hyperbolic plane. In International symposium on graph drawing, pages 355–366. Springer, 2011.
  60. Estimating the support of a high-dimensional distribution. Neural computation, 13(7):1443–1471, 2001.
  61. Light field-based face presentation attack detection: reviewing, benchmarking and one step further. IEEE Transactions on Information Forensics and Security, 13(7):1696–1709, 2018.
  62. 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.
  63. Facebagnet: Bag-of-local-features model for multi-modal face anti-spoofing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0–0, 2019.
  64. Hyperbolic neural networks++. arXiv preprint arXiv:2006.08210, 2020.
  65. Flip: Cross-domain face anti-spoofing with language guidance. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19685–19696, 2023.
  66. Rethinking domain generalization for face anti-spoofing: Separability and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24563–24574, 2023.
  67. Support vector data description. Machine learning, 54:45–66, 2004.
  68. A. Ungar. A gyrovector space approach to hyperbolic geometry. Springer Nature, 2022.
  69. A. A. Ungar. Analytic hyperbolic geometry: Mathematical foundations and applications. World Scientific, 2005.
  70. 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.
  71. Improving cross-database face presentation attack detection via adversarial domain adaptation. In 2019 International Conference on Biometrics (ICB), pages 1–8. IEEE, 2019.
  72. Cross-domain face presentation attack detection via multi-domain disentangled representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6678–6687, 2020.
  73. Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection. IEEE Transactions on Information Forensics and Security, 16:56–69, 2020.
  74. Face live detection method based on physiological motion analysis. Tsinghua Science & Technology, 14(6):685–690, 2009.
  75. Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, 10(4):746–761, 2015.
  76. Unsupervised discovery of the long-tail in instance segmentation using hierarchical self-supervision. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2603–2612, 2021.
  77. 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.
  78. T. Yu and C. M. De Sa. Numerically accurate hyperbolic embeddings using tiling-based models. Advances in Neural Information Processing Systems, 32, 2019.
  79. Revisiting pixel-wise supervision for face anti-spoofing. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(3):285–295, 2021.
  80. Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 151–160, 2019.
  81. 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.
  82. Structure destruction and content combination for face anti-spoofing. In 2021 IEEE International Joint Conference on Biometrics (IJCB), pages 1–6. IEEE, 2021.
  83. Feathernets: Convolutional neural networks as light as feather for face anti-spoofing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019.
  84. A face antispoofing database with diverse attacks. In 2012 5th IAPR international conference on Biometrics (ICB), pages 26–31. IEEE, 2012.
  85. Face anti-spoofing based on multi-layer domain adaptation. In 2019 IEEE international conference on multimedia & expo workshops (ICMEW), pages 192–197. IEEE, 2019.
  86. Generative domain adaptation for face anti-spoofing. In European Conference on Computer Vision, pages 335–356. Springer, 2022.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now