Abstract
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This paper proposes a new face recognition method based on Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Simplified Fuzzy ARTMAP (SFAM). Combination of PCA and LDA is used for improving the capability of LDA and PCA when used alone. Neural classifier, SFAM, is used to reduce the number of misclassifications. Experiment is conducted on ORL database and results demonstrate SFAM’s efficiency as a recognizer. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated.KeywordsFace recognitionPrincipal Component AnalysisLinear Discriminant AnalysisNeural NetworkSimplified Fuzzy ARTMAP
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