Facial Expression Recognition Using Local Feature Selection and the Extended Nearest Neighbor Algorithm

Gabor filters are used to extract holistic feature in facial expression recognition. However, local subtle features can't be extracted effectively and it results in large amounts of data redundancy. In this paper, we proposed a novel facial expression recognition method based on the selection of local Gabor features and the extended nearest neighbor algorithm. The Gabor filter and radial encode is used firstly to divide the expression image into local regions, then PCA and FLD is adopted for feature selection, finally the extended nearest neighbor algorithm is applied to classify the facial expression data. Experiments and analysis conducted on Japanese Female Facial Expression (JAFFE) Database show that this method achieves better efficiency and effectiveness.

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