Evaluation of face recognition system using Support Vector Machine

Face recognition is an interest subject in pattern recognition study for machine learning applications. It is a non-intrusive system which requires minimal participation from user in order to perform identification tasks. In this paper we present a face recognition system based on Support Vector Machine (SVM) which acts as a multiclass classifier. The performance of this system is evaluated using Yale database with various facial expressions and illumination conditions. This method train and test the images with raw image data of 625 features. The result has achieved an encouraging recognition rates compares to Principal Component Analysis method (PCA).