Face Liveness Detection Based on the Improved CNN with Context and Texture Information  

作　　者：GAO Chenqiang LI Xindou ZHOU Fengshun MU Song 

机构地区：[1]School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China [2]Chongqing Key Laboratory of Signal and Information Processing,Chongqing 400065,China

出　　处：《Chinese Journal of Electronics》2019年第6期1092-1098,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61571071);Chongqing Research Program of Basic Research and Frontier Technology(No.cstc2018jcyj AX0227)

摘　　要：Face liveness detection,as a key module of real face recognition systems,is to distinguish a fake face from a real one.In this paper,we propose an improved Convolutional neural network(CNN)architecture with two bypass connections to simultaneously utilize low-level detailed information and high-level semantic information.Considering the importance of the texture information for describing face images,texture features are also adopted under the conventional recognition framework of Support vector machine(SVM).The improved CNN and the texture feature based SVM are fused.Context information which is usually neglected by existing methods is well utilized in this paper.Two widely used datasets are used to test the proposed method.Extensive experiments show that our method outperforms the state-of-the-art methods.Face liveness detection, as a key module of real face recognition systems, is to distinguish a fake face from a real one. In this paper, we propose an improved Convolutional neural network(CNN) architecture with two bypass connections to simultaneously utilize low-level detailed information and high-level semantic information.Considering the importance of the texture information for describing face images, texture features are also adopted under the conventional recognition framework of Support vector machine(SVM). The improved CNN and the texture feature based SVM are fused. Context information which is usually neglected by existing methods is well utilized in this paper. Two widely used datasets are used to test the proposed method. Extensive experiments show that our method outperforms the state-of-the-art methods.

关 键 词：FACE LIVENESS detection DEEP learning CONTEXT INFORMATION TEXTURE INFORMATION 

分 类 号：TN[电子电信]