A COMPARISON OF EIGENVALUE METHODS FOR PRINCIPAL COMPONENT ANALYSIS

We compare four commonly used eigenvector methods, namely cyclic Jacobi s method of iteration, Wiedlandt s deflation, Hotelling s deflation, and MATLAB s own eigenvalue method for the success of face recognition which is based on Principal Component Analysis (PCA).We report that the highest recognition rate is equally achieved by MATLAB s eigenvalue method and Hotelling s deflation. The former is observed to be the fastest for large numbers of dominant eigenfaces while scaling the best with the number of computational cores. On the other hand, the latter has a brief and open source code that can be easily modified for a given purpose. We further investigate the impact of altering face images to improve the recognition rate. Different sets of images have been obtained from two well-known face databases, various effects using imaging filters have been applied to them, and the resulting sets have been used as both training and test sets. Recognition rates reveal that some of these fitered sets can be even better candidates for training and testing than the original sets