Face Recognition between Two Person using Kernel Principal Component Analysis and Support Vector Machines

Abstract

Face recognition is gaining enormous interest nowadays. However, the technical challenges to "teach" a computer to recognize faces have been very difficult. Many methods and approaches have been proposed in the literature. This paper presents a face recognition method based on the combined kernel principal component analysis (KPCA) and support vector machine (SVM) methods. First, the KPCA method is utilized to extract features from the input images. The SVM method is then applied to these extracted features to classify the input images. We compare the performance of this face recognition method to other commonly-used methods. Our experiments show that the combination of KPCA and SVM achieves a higher performance compared to the nearest neighbor classifier, support vector machine, and the combination of kernel principal component analysis and nearest neighbor classifier.