Multiple-Exemplar Discriminant Analysis for Face Recognition

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.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Rama Chellappa, Shaohua Kevin Zhou
• Keywords: Computer Vision, ICPR 2004, Linear Discriminant Analysis, Multiple-exemplar Discriminant Analysis, Regular Pattern Recognition