纹理和深度特征增强的双流人脸呈现攻击检测方法  

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

作　　者：孙锐[1,2] 冯惠东 孙琦景 单晓全 张旭东[1] SUN Rui;FENG Huidong;SUN Qijing;SHAN Xiaoquan;ZHANG Xudong(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601;Anhui Key Laboratory of Industry Safety and Emergency Technology,Hefei University of Technology,Hefei 230601)

机构地区：[1]合肥工业大学计算机与信息学院,合肥230601 [2]合肥工业大学工业安全与应急技术安徽省重点实验室,合肥230601

出　　处：《模式识别与人工智能》2023年第3期242-251,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金面上项目(No.61876057);安徽省自然科学基金项目(No.2208085MF158);安徽省重点研发计划-科技强警专项项目(No.202004d07020012)资助。

摘　　要：人脸呈现攻击是一种利用照片、视频等将人脸通过媒介呈现在摄像头前欺骗人脸识别系统的技术.现有的人脸呈现攻击检测方法大多采用深度特征辅助监督分类,忽略有效的细粒度信息以及深度信息与纹理信息的相互联系.因此,文中提出纹理和深度特征增强的双流人脸呈现攻击检测方法.一端网络通过中心差分卷积网络提取比原始卷积网络更鲁棒的欺骗人脸纹理模式.另一端网络通过生成对抗网络生成深度图的深度线索,提高对外观变化和图像质量差异的稳定性.在特征增强模块中,设计中心边缘损失,对两类互补特征进行融合和增强.在4个数据集上的实验表明,文中方法在数据集内以及跨数据集的测试中都取得较优性能.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.

关 键 词：呈现攻击检测 人脸反欺骗 生成对抗网络 特征增强 中心边缘损失 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]