Abstract
Recently, the generalization capability of face anti-spoofing models has taken the attention of both industry and academia. Among all the problems, domain shift and unknown attacks are the most serious problems affecting generalization performance. Existing work usually focuses on dealing with one of the above problems. In this paper, we address a challenging but practical problem, open-set single domain generalization-based (OSSDG-based) face anti-spoofing, simultaneously addressing these two problems with limited training data. We propose a novel unknown-aware diverse prompt learning framework, which mines the visual-language pre-training knowledge for OSSDG-based face anti-spoofing by text prompt generation and unknown-aware learning regularization. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on both known classes and unknown attacks in cross-scenario domains with a single training domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu, Z., Qin, Y., Li, X., Zhao, C., Lei, Z., Zhao, G.: Deep learning for face anti-spoofing: a survey. TPAMI 45(5), 5609–5631 (2023)
Jia, Y., Zhang, J., Shan, S., Chen, X.: Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing. PR 115, 107888 (2021)
Liu, Y., Stehouwer, J., Jourabloo, A., Liu, X.: Deep tree learning for zero-shot face anti-spoofing. In: CVPR, pp. 4680–4689 (2019)
Jiang, F., et al.: Open-set single-domain generalization for robust face anti-spoofing. IJCV, 1–22 (2024)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748–8763 (2021)
Srivatsan, K., Naseer, M., Nandakumar, K.: Flip: cross-domain face anti-spoofing with language guidance. In: ICCV, pp. 19 685–19 696 (2023)
Liu, A., et al.: CFPL-FAS: class free prompt learning for generalizable face anti-spoofing. In: CVPR, pp. 222–232 (2024)
Yan, P., Liu, X., Zhang, P., Lu, H.: Learning convolutional multi-level transformers for image-based person re-identification. Vis. Intell. 1(1), 24 (2023)
Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR, pp. 389–398 (2018)
Yu, Z., Cai, R., Cui, Y., Liu, A., Chen, C.: Visual prompt flexible-modal face anti-spoofing. arXiv preprint arXiv:2307.13958 (2023)
Li, H., Li, W., Cao, H., Wang, S., Huang, F., Kot, A.C.: Unsupervised domain adaptation for face anti-spoofing. TIFS 13(7), 1794–1809 (2018)
Li, Q., Wang, W., Xu, C., Sun, Z., Yang, M.-H.: Learning disentangled representation for one-shot progressive face swapping. In: TPAMI (2024)
Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: NeurIPS, pp. 2479–2488 (2017)
Zhang, J., Huang, J., Jin, S., Lu, S.: Vision-language models for vision tasks: a survey. arXiv preprint arXiv:2304.00685 (2023)
Li, Z., Lv, X., Yu, W., Liu, Q., Lin, J., Zhang, S.: Face shape transfer via semantic warping. Vis. Intell. 2(1), 1–11 (2024)
Peng, S., Zhu, X., Yi, D., Qian, C., Lei, Z.: Formulating facial mesh tracking as a differentiable optimization problem: a backpropagation-based solution. Vis. Intell. 21(1), 1–12 (2024)
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. IJCV 130(9), 2337–2348 (2022)
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Conditional prompt learning for vision-language models. arXiv preprint arXiv:2203.05557 (2022)
Bahng, H., Jahanian, A., Sankaranarayanan, S., Isola, P.: Exploring visual prompts for adapting large-scale models. arXiv preprint arXiv:2203.17274 (2022)
Zang, Y., Li, W., Zhou, K., Huang, C., Loy, C.C.: Unified vision and language prompt learning. arXiv preprint arXiv:2210.07225 (2022)
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: ICB, pp. 26–31 (2012)
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: Proceedings of International Conference of the Biometrics Special Interest Group, pp. 1–7 (2012)
Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. TIFS 10(4), 746–761 (2015)
Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: FG, pp. 612–618 (2017)
Heusch, G., George, A., Geissbühler, D., Mostaani, Z., Marcel, S.: Deep models and shortwave infrared information to detect face presentation attacks. TBIOM 2(4), 399–409 (2020)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. TIFS 11(8), 1818–1830 (2016)
Wang, G., Han, H., Shan, S., Chen, X.: Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection. TIFS 16, 56–69 (2020)
Wang, C.-Y., Lu, Y.-D., Yang, S.-T., Lai, S.-H.: PatchNet: a simple face anti-spoofing framework via fine-grained patch recognition. In: CVPR, pp. 20 281–20 290 (2022)
Huang, H.-P., et al.: Adaptive transformers for robust few-shot cross-domain face anti-spoofing. In: ECCV, pp. 37–54 (2022)
Acknowledgments
This work was supported in part by the National Key Research and Development Program of China (Grant No. 2022YFC3310400), in part by the Natural Science Foundation of China (Grant Nos. U23B2054, 62076240, 62102419, 62276263 and 62406133), in part by the Beijing Municipal Natural Science Foundation (Grant No. 4222054), in part by the Natural Science Foundation of Hunan Province (Grant No.2024JJ6389) and in part by the Hengyang Science and Technology Plan Project (Grant No.202330046190).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, F., Li, Q., Wang, W., Liu, B., Sun, Z. (2025). Unknown-Aware Diverse Prompt Learning for Open-Set Single Domain Generalization-Based Face Anti-spoofing. In: Yu, S., et al. Biometric Recognition. CCBR 2024. Lecture Notes in Computer Science, vol 15352. Springer, Singapore. https://doi.org/10.1007/978-981-96-1068-6_26
Download citation
DOI: https://doi.org/10.1007/978-981-96-1068-6_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-1067-9
Online ISBN: 978-981-96-1068-6
eBook Packages: Computer ScienceComputer Science (R0)