Analysis of Principal Component Analysis-Based and Fisher Discriminant Analysis-Based Face Recognition Algorithms

Abstract

A facial recognition system is a system for automatically recognizing a person from a digital image as the human eye recognizes. Here two algorithms principal component analysis (PCA) and Fisher discriminant analysis (FDA) of holistic approach of information theory have been analyzed. Recognition process comprises the two steps: training and testing. In the training phase a set of the eigenvectors of the covariance matrix of the images used for training. These eigenvectors are also called as eigenfaces. In testing phase when a new input image is given for recognition, this image will be projected into the eigenspace by using the already calculated eigenvectors. Test image will be compared with all the images in the eigenspace and measures the euclidean distance. The image with the lowest Euclidean distance is the matched image if the distance lies below some threshold value. Both algorithms works in the same manner, the difference lies in the calculation of face space. These two algorithms are evaluated experimentally on two databases each with the moderate subject size. Analysis and experimental results indicates that the PCA works well when the lightening variation is small. FDA works gives better accuracy in facial expression