Evaluation of the independent component analysis algorithm for face recognition under varying conditions

Face Recognition has been a major topic of research for many years and several approaches have been developed, among which the Principal Component Analysis (PCA) algorithm using Eigenfaces is the most popular. Eigenfaces optimally extract a reduced basis set that minimizes reconstruction error for the face class prototypes. The method is based on second-order pixel statistics and does not address higher-order statistical dependencies such as relationships among three or more pixels. Independent Component Analysis (ICA) is a recently developed linear transformation method for finding suitable representations of multivariate data, such that the components of the representation are as statistically independent as possible. The face image class prototypes in ICA are considered to be a linear-mixture of some unknown set of basis images that are assumed to be statistically independent, in the sense that the pixel values of one basis image cannot be predicted from that of another. This research evaluates the performance of ICA for face recognition under varying conditions like change of expression, change in illumination and partial occlusion. We compare the results with that of standard PCA, employing the Yale face database for the experiments and the results show that ICA is better under certain conditions.