An eigenfaces-based automatic face recognition system

Abstract

The problem of automatic face recognition (AFR) alone is a difficult task that involves detection and location of faces in a cluttered background, facial feature extraction, subject identification and verification. The main challenge lies in facial feature extraction. This should reduce the intra-person variability (due to changes in geometry, illumination, gesture, and biological changes) and increase the inter-person variability. Various approaches have previously been proposed, including the eigenfaces for which satisfactory experimental results have been reported. The eigenfaces approach assumes that the data is intrinsically low-dimensional. This contribution presents an eigenfaces-based AFR, that guarantees the low-dimensionality assumption by preprocessing steps and multiple eigenspaces. The necessity for pre-processing steps has already been recognized by other groups. In this paper, the need for multiple eigenspaces and the corresponding operative criterion is established.