Face Recognition Using Gabor features And PCA

Face recognition is a hot research topic in the fields of pattern recognition and computer vision, which has been found a widely used in many applications, such as verification of credit card, security access control, and human computer interface. As a result, numerous face recognition algorithms have been proposed, and surveys in this area can be found. Although many approaches for face recognition have been proposed in the past, none of them can overcome the main problem of lighting, pose and orientation. For a real time face recognition system, these constraints are to be a major Analysis (PCA) .These methods challenge which has to be addressed. In this proposed system, a methodology is given for improving the robustness of a face recognition system based on two well-known statistical modelling methods to represent a face image: Principal Component extract the discriminates features from the face. Preprocessing of human face image is done using Gabor wavelets which eliminates the variations due to pose, lighting and features to some extent. PCA extract low dimensional and discriminating feature vectors and these feature vectors were used for classification. The classification stage uses nearest neighbour as classifier. This proposed system will use the YALE face data base with 100 frontal images corresponding to10 different subjects of variable illumination and