In this thesis,a novel method for face recognition system is proposed.It is a three stage process,first features are extracted ,as per requirements features are selected then faces are classified according to their respective classes. In section I,Principal Component Analysis (PCA) ,for feature extraction is used and Euclidean distance is used for identification. In section II,a face recognition system based on enhanced local Gabor binary sequence is used for effective face feature extraction and neural network is being used for classification.As local binary pattern(LBP) is very resistive to illumination changes ,it is a good option for coding fine details of facial visual aspect and texture. In section III,Back-Propagation network (BPN) is being used in various fields.Rule of thumb or ”error and trial method” are usually used to determine different parameters like learning rate and number of hidden neurons.Therefore ,a simulated-annealing-based approach denoted by SA+ BPN is proposed to get the optimum parameter settings for the network. The proposed method is resistant to slight variation in imaging conditions and poses. The algorithms that have been applied are tested on ORL Face Database and Yale Database
