Multiple-Exemplar Discriminant Analysis for Face Recognition

Authors:
S. Kevin Zhou;Rama Chellappa
Affiliations:
University of Maryland, College Park;University of Maryland, College Park
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Year:
2004

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Abstract

Face recognition is characteristically different from regular pattern recognition and, therefore, requires a different discriminant analysis other than linear discriminant analysis (LDA). LDA is a single-exemplar method in the sense that each class during classification is represented by a single exemplar, i.e. the sample mean of the class. In this paper, we present a multiple-exemplar discriminant analysis (MEDA) where each class is represented using several exemplars or even the whole available sample set. The proposed approach produces improved classification results when tested on a subset of FERET database where LDA is ineffective.