Gabor features and LDA based face recognition with ANN classifier

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 challenge which has to be addressed. In this proposed work, a methodology is adopted for improving the robustness of a face recognition system based on two well-known statistical modeling methods to represent a face image: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). These methods extract the discriminant 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 and LDA extract low dimensional and discriminating feature vectors and these feature vectors were used for classification. The classification stage uses Backpropagation neural network (BPN) as classifier. This proposed system has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects of variable illumination and facial expressions. The results are compared with standard eigen face method using distance measure as classifier. The system gives a better recognition rate compared to other standard techniques.