Appliance Of Sparse Representation Based Face Recognition

Face recognition is one of the research focuses among the area of image processing. And it has already beenwidely used among many areas such as pattern recognition, artificial intelligence, and computer vision and so on.Sparse representation based face recognition, with a simple theoretical basis and a robust performance, hasattracted close attention from many researchers. After studying many related articles, this thesis has found that itis the face occlusion, illumination variation, and poor face alignment that stop the face recognition from practicalappliance. This thesis is aimed to handle these problems. The mainly researching contents in this thesis conclude:This thesis starts from introducing the theory of sparse representation. According to the theory, this thesisdescribes a traditional scheme which is called face recognition via sparse representation based classification. Then,this thesis provides a new scheme called face recognition via robust sparse representation, which is more robust tooutliers in the image. The face recognition via robust sparse representation algorithm has a good performance indealing with face occlusion and illumination variation. And this thesis introduces the use of random features intothe face recognition scheme.In order to handle the poor face alignment problem, this thesis makes use of a matrix rank minimizationbased face alignment model. For the sake of solving the linearized convex optimization problem in the facealignment model, this thesis proposes an algorithm called the inexact augmented Lagrange multipliers algorithmwhich has a good performance.This thesis will carry out experiments between proposed algorithms and the popular PCA, LDA algorithmson LFW, ORL and Yale database. Based on the experiment results, this thesis finds that sparse representationbased face recognition algorithms are better than the popular PCA algorithm and LDA algorithm. And when thereare outliers in the face image, the performance of face recognition via robust sparse representation algorithm isbetter than the traditional face recognition via sparse representation based classification algorithm.