Multiple-exemplar discriminant analysis for face recognition

TitleMultiple-exemplar discriminant analysis for face recognition
Publication TypeConference Papers
Year of Publication2004
AuthorsZhou SK, Chellappa R
Conference NamePattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Date Published2004/08//
Keywordsanalysis;, database;, databases;, discriminant, Face, FERET, multiple-exemplar, recognition;, visual

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. 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.