Open AccessFace Liveness Detection Based on the Improved CNN with Context and Texture Information
Author(s) -
Gao Chenqiang,
Li Xindou,
Zhou Fengshun,
Mu Song
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.07.012
Subject(s) - liveness , computer science , convolutional neural network , artificial intelligence , support vector machine , face (sociological concept) , pattern recognition (psychology) , context (archaeology) , feature (linguistics) , texture (cosmology) , facial recognition system , key (lock) , face detection , computer vision , image (mathematics) , theoretical computer science , paleontology , social science , linguistics , philosophy , computer security , sociology , biology
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.