Texture and Depth Feature Enhancement Based Two-Stream Face Presentation Attack Detection Method

被引：0
|
作者：
Sun R. [1 ,2 ]
Feng H. [1 ,2 ]
Sun Q. [1 ,2 ]
Shan X. [1 ,2 ]
Zhang X. [1 ]
机构：
[1] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei
[2] Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2023年 / 36卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Center Edge Loss; Face Anti-Spoofing; Feature Enhancement; Generative Adversarial Network; Presentation Attack Detection;
D O I：
10.16451/j.cnki.issn1003-6059.202303004
中图分类号：
学科分类号：
摘要：
Face presentation attack is a technology using photos, videos and other media to present faces in front of cameras to spoof face recognition systems. Most of the existing face presentation attack detection methods apply depth feature for supervised classification, while ignoring the effective fine-grained information and the correlation between depth information and texture information. Therefore, a texture and depth feature enhancement based two-stream face presentation attack detection method is proposed. One end of the network extracts the facial texture features with a more robust deception texture pattern than the original convolution network through the central differential convolution network. The other end of the network generates the depth information of the depth map through generative adversarial network to improve the robustness to the appearance changes and image quality differences. In the feature enhancement module, a central edge loss is designed to fuse and enhance two types of complementary features. The experimental results on 4 datasets show that the proposed method achieves superior performance in both intra-data set and cross-data set tests. © 2023 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.
引用
收藏
页码：242 / 251
页数：9
相关论文
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