Cost Sensitive Face Security Verification Based on Limited Expression-Pose Pattern Sparse Representation Model

Abstract

Face security verification has been recognized as a cost sensitive classification problem. To deal with this problem, many cost sensitive classifiers have been proposed to alleviate the facial variation. However, no suficient attention is paid to the research on sparse representation cost sensitive face verification. In this paper, we proposed a coarse to find face security verification method, called cost sensitive face verification based on limited expression-pose pattern (CSFV_LEP) for security verification task. The main contributions of the proposed method are as follows: (1) a discrimination dictionary is established in a common way to discriminate whether visitor is an internal member; (2) a confirmation dictionary, which contains only limited expression-pose details, is used to confirm whether this is the correct classification. Meanwhile, we use adaptive weight matrix from similarity information to enhance the robustness of the two dictionaries. Experiments show that, the proposed method has high verifiable and ideal secure performance according to accuracy and efficiency, and contribute to reduce cost penalization during the sparse coding stages.