Few-Shot Face Spoofing Detection Using Feedforward Learning Network

被引：0
|
作者：
Song Y. [1 ,2 ,3 ,4 ,5 ]
Sun W.-Y. [1 ,2 ,3 ,4 ,5 ]
Chen C.-S. [1 ,2 ,3 ,4 ,5 ]
机构：
[1] College of Electronics and Information Engineering, Shenzhen University, Shenzhen
[2] Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen
[3] Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen
[4] Guangdong Laboratory of Artificial Intelligence and Digital Economy, Shenzhen University, Shenzhen
[5] Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2020年 / 43卷 / 05期
关键词：
Face spoofing detection; Feedforward learning network; Representation learning;
D O I：
10.13190/j.jbupt.2020-068
中图分类号：
学科分类号：
摘要：
In order to overcome the limitations of the existing face spoofing detection methods under few-shot face anti-spoofing applications, this paper proposes to use feedforward learning network for face anti-spoofing. The convolutional filters are learned unsupervisedly from the images in a feedforward manner. The feedforward learning network is adapted in the spoof face detection applications by using face anti-spoofing task-oriented convolutional filters learned from the training images. The eigenvectors that correspond to the smallest eigenvalues obtained from the principle component analysis transform are used as convolution filters for extracting features from images. The method is evaluated on some benchmark datasets including CASIA-FASD dataset, Idiap Replay-Attack dataset and OULU-NPU dataset. Experiments show that under the cross presentation attack detection experiments, the proposed method significantly improves the classification accuracy of existing methods. © 2020, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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页码：48 / 56
页数：8
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