PCA-LDA based face recognition system & results comparison by various classification techniques

Face recognition has a major impact in security measures which makes it one of the most appealing areas to explore. To perform face recognition, researchers adopt mathematical calculations to develop automatic recognition systems. As a face recognition system has to perform over wide range of database, dimension reduction techniques become a prime requirement to reduce time and increase accuracy. In this paper, face recognition is performed using Principal Component Analysis followed by Linear Discriminant Analysis based dimension reduction techniques. Sequencing of this paper is preprocessing, dimension reduction of training database set by PCA, extraction of features for class separability by LDA and finally testing by nearest mean classification techniques. The proposed method is tested over ORL face database. It is found that recognition rate on this database is 96.35% and hence showing efficiency of the proposed method than previously adopted methods of face recognition systems.