In this paper

a facial expression recognition method based on feature fusion and fuzzy kernel discriminant analysis (FKDA)is proposed This method firstly locates 34 landmark points from each facial image as the Geometric features of the facial image Then

these landmark points are converted into a labeled graph (LG)vector using the Gabor wavelet transformation method， and the LG vector are used as the Gabor feature vector of the facial image Both Geometric feature and Gabor feature are further fused using the canonical correlation analysis (CCA)as the final input facial features for recognition The FKDA method is finally used to further extract the discriminative expression features for classification and the nearest neighbor classifier is used to this goal Experiments on both Japanese Female Facial Expression (JAFFE)database and the Ekman’s ‘Pictures of Facial Affect’ database demonstrate the better performance of the proposed method