Liveness detection of occluded face based on dual-modality convolutional neural network

作     者：Ming Yue Li Wenmin Xu Siya Gao Lifang Zhang Hua Shao Sujie Yang Huifeng Ming Yue;Li Wenmin;Xu Siya;Gao Lifang;Zhang Hua;Shao Sujie;Yang Huifeng

作者机构：State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China State Grid Hebei Information and Telecommunication BranchShijiazhuang 050013China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2021年第28卷第4期

页      面：1-12页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Science and Technology Project of State Grid Corporation of China(SGHEXT00YJJS1900050) 

主　　题：face deception face occlusion near-infrared image residual network feature fusion 

摘      要：Facial recognition has become the most common identity authentication technologies. However, problems such as uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are a few pieces of research on face liveness detection under occlusion conditions. This paper designs a face recognition technique suitable for different degrees of facial occlusion, which employs the facial datasets of near-infrared(NIR) images and visible(VIS) light images to examine the single-modality detection accuracy rate(experimental control group) and the corresponding high-dimensional features through the residual network(ResNet). Based on the idea of data fusion, we propose two feature fusion methods. The two methods extract and fuse the data of one and two convolutional layers from two single-modality detectors respectively. The fusion of high-dimensional features apply a new ResNet to get the dual-modality detection accuracy. And then, a new ResNet is applied to test the accuracy of dual-modality detection. The experimental results show that the dual-modality face liveness detection model improves face live detection accuracy and robustness compared with the single-modality. The fusion of two-layer features from the single-modality detector can also improve face detection accuracy by utilizing the above-mentioned dual-modality detector, and it doesn’t increase the algorithm’s complexity.