FACE RECOGNITION - EIGENFACE, ELASTIC MATCHING, AND NEURAL NETS

This paper is a comparative study of three recently proposed algorithm s for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyze d under a common statistical decision framework, they were evaluated e xperimentally on four individual data bases, each with a moderate subj ect size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenfac e algorithm, which is essentially a minimum distance classifier works well when lighting variation is small. Its performance deteriorates si gnificantly as lighting variation increases. The elastic matching algo rithm, on the other hand, is insensitive to lighting, face position, a nd expression variations and therefore is more versatile. The performa nce of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.