基于特征融合和模糊核判别分析的面部表情识别方法
周晓彦1,2, 郑文明3, 邹采荣1),4) 赵力1)1,4, 赵力1(1.东南大学信息科学与工程学院,南京 210096;2.南京信息工程大学电子与信息工程学院,南京 210044;3.东南大学学习科学研究中心,南京 210096;4.佛山科技学院,佛山 528000) 摘 要
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。
关键词
Facial Expression Recognition Based on Feature Fusion and Fuzzy Kernel Discriminant Analysis
() Abstract
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
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