Open-Set Single-Domain Generalization for Robust Face Anti-Spoofing-Reference-Cited by-同舟云学术

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Open-Set Single-Domain Generalization for Robust Face Anti-Spoofing

Published:2024-06-03 Issue: Volume: Page:
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Jiang Fangling,Li Qi,Wang Weining,Ren Min,Shen Wei,Liu Bing,Sun Zhenan

Funder

National Key Research and Development Program of China

Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Scientific Research Foundation of Department of Education of Hunan Province

Hengyang Science and Technology Plan Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11263-024-02129-0.pdf

Reference69 articles.

1. Abduh, L., & Ivrissimtzis, I. (2020). Use of in-the-wild images for anomaly detection in face anti-spoofing. arXiv:2006.10626

2. Arashloo, S. R. (2020). Unseen face presentation attack detection using sparse multiple kernel fisher null-space. IEEE Transactions on Circuits and Systems for Video Technology, 31(10), 4084–4095.

3. Arashloo, S. R. (2023). Unknown face presentation attack detection via localized learning of multiple kernels. IEEE Transactions on Information Forensics and Security, 18, 1421–1432.

4. Boulkenafet, Z., Komulainen, J., & Hadid, A. (2016). Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security, 11(8), 1818–1830.

5. Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., & Hadid, A. (2017). Oulu-npu: A mobile face presentation attack database with real-world variations. In Proceedings of international conference on automatic face and gesture recognition (pp. 612–618).

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