Face anti‐spoofing from the perspective of data sampling
Author:
Affiliation:
1. University of OuluCenter for Machine Vision and Signal Analysis (CMVS)OuluFinland
2. University of Oulu Faculty of Information Technology and Electrical EngineeringCenter for Machine Vision and Signal Analysis (CMVS)OuluFinland
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/ell2.12692
Reference32 articles.
1. Face anti‐spoofing with refined triplet loss and multi‐level attention constraint network
2. Face presentation attack detection based on optical flow and texture analysis;Li L.;J. King Saud Univ., Comput. Inf. Sci.,2022
3. Gan J. Li S. Zhai Y. Liu C.:3D convolutional neural network based on face anti‐spoofing. In:2017 2nd International Conference on Multimedia and Image Processing (ICMIP) pp. 1–5.IEEE Piscataway NJ(2017)
4. An Adaptive Spatio-temporal Global Sampling for Presentation Attack Detection
5. Wang Z. Yu Z. Zhao C. Zhu X. Qin Y. Zhou Q. Lei Z.:Deep spatial gradient and temporal depth learning for face anti‐spoofing. In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 5042–5051.IEEE Piscataway NJ(2020)
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing;2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA);2023-10-16
2. Saliency-Based Video Summarization for Face Anti-Spoofing;2023
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