提出了基于特征融合和模糊核判别分析（FKDA）的面部表情识别方法。首先，从每幅人脸图像中手工定位34个基准点，作为面部表情图像的几何特征，同时采用Gabor小波变换方法对每幅表情图像进行变换，并提取基准点处的Gabor小波系数值作为表情图像的Gabor特征；其次，利用典型相关分析技术对几何特征和Gabor特征进行特征融合，作为表情识别的输入特征；然后，利用模糊核判别分析方法进一步提取表情的鉴别特征；最后，采用最近邻分类器完成表情的分类识别。通过在JAFFE国际表情数据库和Ekman“面部表情图片”数据库上的实验，证实了所提方法的有效性。

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