Face Recognition Using Principal Component Analysis

Face recognition is one of the most relevant applications of image analysis. It’s an efficient task (true challenge) to build an automated system with equal human ability to face recognised. Face is a complex 3D visual model and developing a computational model for face recognition is a difficult task. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is combination of two stages – Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The proposed method has been tested on Oracle Research Laboratory (ORL) face database containing 400 images (40 classes). A recognition score for the test lot is calculated by considering almost all the variants of feature extraction. The test results gave a recognition rate of 99.50%. Keywords—Face recognition, Principal component analysis (PCA), Artificial Neural network (ANN), Eigenvector, and Eigenfaces.