Face recognition using sparce reprasentations and p-laplacian

Face recognition is one of the most important identification tools in biometrics. Nowadays, the topic of face recognition has many applications in various fields, including public security, identity identification, protection of important and sensitive places, access control, video surveillance, and so on. Two important issues in face recognition applications are speed and accuracy in detection. Various studies have shown that face recognition through Sparce Representation Classification (SRC) works very well.The purpose of this paper is to propose a fast and efficient method for the sparse representation-based face recognition .Due to the fact that retrieving the Sparce Representation based on L1 norm optimization for a large dictionary has a large computational volume, a Smooth L0 norm optimization (SL0) method is used. Also, due to the fact that one of the challenges of face recognition is the existence of brightness changes in images, so we use the P- Laplacein algorithm in the feature extraction step to give us more complete information about the face image by recognizing the edge. As the simulation results on the Extended Yale B and AR database show, the proposed hybrid method has a higher detection rate than the sparse display method.