纹理和深度特征增强的双流人脸呈现攻击检测方法

doi:10.16451/j.cnki.issn1003-6059.202303004

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Abstract 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.

Key words： Presentation Attack Detection Face Anti-Spoofing Generative Adversarial Network Feature Enhancement Center Edge Loss

Received: 03 November 2022

ZTFLH:

TP391

Fund:General Project of National Natural Science Foundation of China(No.61876057), Natural Science Foundation of Anhui Province(No.2208085MF158), Key Research and Development Plan of Anhui Province-Special Project for Strengthening police with Science and Technology(No.202004D07020012)

Corresponding Authors: SUN Rui, Ph.D., professor. His research interests include computer vision and machine learning.

About author:: FENG Huidong, master student. His research interests include image information processing and computer vision. SUN Qijing, master student. His research interests include image information processing and computer vision. SHAN Xiaoquan, master student. His research interests include image information processing and computer vision. ZHANG Xudong, Ph.D., professor. His research interests include intelligent information processing and machine vision.

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	SUN Rui
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Cite this article:

SUN Rui,FENG Huidong,SUN Qijing等. Texture and Depth Feature Enhancement Based Two-Stream Face Presentation Attack Detection Method[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(3): 242-251.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202303004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I3/242

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