One-class learning for face anti-spoofing via pseudo-negative sampling
Author:
Funder
Ministry of Science and ICT, South Korea
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17739-y.pdf
Reference44 articles.
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2. Rusia MK, Singh DK (2023) A comprehensive survey on techniques to handle face identity threats: challenges and opportunities. Multimedia Tools Appl 82(2):1669–1748
3. European union agency for cybersecurity (2022) Remote identity proofing: attacks & countermeasures. Publications Office, LU. https://doi.org/10.2824/183066
4. Atoum Y, Liu Y, Jourabloo A, Liu X (2017) Face anti-spoofing using patch and depth-based CNNs. In: 2017 IEEE International joint conference on biometrics (IJCB), pp 319–328
5. 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: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5295–5305
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