Pose Variant Based Comparative Analysis of PCA and LDA

Principal component analysis (PCA) and Fisher discriminate analysis (FDA) of holistic approach of Information theory have been analyzed. Two steps for recognition are taken: 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. LDA works gives better accuracy in facial expression.