Expression Recognition in Sparse Principal Component Combine Low-Rank Decomposition Architecture

Expression Recognition in Sparse Principal Component Combine Low-Rank Decomposition Architecture

In order to solve the problem of low face recognition rate in uncontrolled scenes, a face recognition algorithm based on sparse representation of principal components and low rank decomposition in uncontrolled scenes is proposed. Firstly, the basic face database is constructed by the data collected by the core basic information platform, and then the classroom photos are collected and the sampled photos are segmented by principal component sparse representation and low rank decomposition algorithm. Finally, the basic face database is used as a sample for matching recognition, and the results not handled in low rank decomposition are compared with those handled in low rank decomposition. The experimental results show that the recognition effect of superimposed low rank decomposition by sparse representation of principal components in uncontrolled scene is robust to the change of light, and the influence on shelter blocking is relatively obvious. The highest recognition accuracy is 92.4%, which achieves a better face recognition effect in uncontrolled scenes. The algorithm is helpful to the research of face recognition, expression recognition and behavior recognition in common uncontrolled scenes.