Abstract
AbstractIn this paper a new facial expression recognition method based on Local Fisher Discriminant Analysis (LFDA) is proposed. LFDA is used to extract the low-dimensional discriminant embedded data representations from the original high-dimensional local binary patterns (LBP) features. The Knearest- neighbor (KNN) classifier with the Euclidean distance is adopted for facial expression classification. The experimental results on the popular JAFFE facial expression database demonstrate that the best accuracy based on LFDA is up to 85.71%, outperforming the used Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA).KeywordsFacial expression recognitionLocal binary patternsLocal Fisher discriminant analysis
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