Facial expression recognition based on fuzzy-LDA/CCA  

基于模糊LDA/CCA的面部表情识别(英文)

作　　者：周晓彦[1,2] 郑文明[3] 邹采荣[1,4] 赵力[1] 

机构地区：[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]东南大学学习科学中心,南京210096 [4]佛山科技学院,佛山528000

出　　处：《Journal of Southeast University(English Edition)》2008年第4期428-432,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China (No.60503023,60872160);the Natural Science Foundation for Universities ofJiangsu Province (No.08KJD520009);the Intramural Research Foundationof Nanjing University of Information Science and Technology(No.Y603)

摘　　要：A novel fuzzy linear discriminant analysis method by the canonical correlation analysis （fuzzy-LDA/CCA）is presented and applied to the facial expression recognition. The fuzzy method is used to evaluate the degree of the class membership to which each training sample belongs. CCA is then used to establish the relationship between each facial image and the corresponding class membership vector, and the class membership vector of a test image is estimated using this relationship. Moreover, the fuzzy-LDA/CCA method is also generalized to deal with nonlinear discriminant analysis problems via kernel method. The performance of the proposed method is demonstrated using real data.提出了一种新颖的基于典型相关分析(CCA)的模糊判别分析方法(fuzzy-LDA/CCA),并应用于面部表情识别问题.首先为每幅表情图像建立一个相关联的类模糊隶属度矢量,用于表示表情图像与基本表情类别的隶属关系,在此基础上应用CCA方法建立表情图像同表情类别的关系表达式,最后通过对表情图像的类隶属度矢量的估计来实现表情的分类.此外,还将fuzzy-LDA/CCA方法在核空间中进行了非线性推广,从而来解决非线性判别分析的问题.实验证明提出的方法获得了更好的识别效果.

关 键 词：fuzzy linear discriminant analysis canonical correlation analysis facial expression recognition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]