Face recognition: eigenface, elastic matching, and neural nets

This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on fou...

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Bibliographic Details
Published inProceedings of the IEEE Vol. 85; no. 9; pp. 1423 - 1435
Main Authors Jun Zhang, Yong Yan, Lades, M.
Format Journal Article
LanguageEnglish
Published IEEE 01.09.1997
Subjects
Algorithm design and analysis
Authentication
Banking
Face recognition
Laboratories
Law enforcement
Neural networks
Pattern matching
Signal analysis
Wavelet analysis
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Summary:This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.
ISSN:0018-9219
DOI:10.1109/5.628712