基于类内分块PCA方法的人脸表情识别

作者：龚婷1,2，胡同森1，田贤忠1 日期：2009-08-18/span> 浏览：3693 查看PDF文档

基于类内分块PCA方法的人脸表情识别

龚婷1,2，胡同森1，田贤忠1
(1.浙江工业大学 信息学院,浙江 杭州 310032; 2.浙江科技学院 信息学院,浙江 杭州 310023)

摘要：主成分分析方法(PCA)是目前广泛应用在人脸等图像识别领域的重要手段。为了更准确地识别人脸的表情信息，有效抽取出图像中对表情识别贡献较大的局部特征，提出了一种类内分块PCA方法对人脸表情进行特征提取。首先对图像进行分块，再对分块得到的所有子图像块利用PCA方法进行鉴别分析，并计算出各类训练样本的子空间，然后计算测试样本到各类子空间的距离，最后输入最近邻分类器得到分类结果。在JAFFE人脸表情库上进行的实验结果表明，使用该方法后获得的识别率优于传统的PCA方法。
关键词：主成分分析方法；特征提取；类内分块PCA；人脸表情识别
中图分类号：TP391文献标识码：A文章编号：1001-4551(2009)07-0074-03

Human face expression recognition based on withinclass modular PCA
GONG Ting1,2, HU Tongsen1, TIAN Xianzhong1
(1.College of Information Engineering, Zhejiang University of Technology, Hangzhou 310032, China;
2.College of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)
Abstract: Principal component analysis (PCA) is the important technique widely used in the areas of images recognition such as human face. Aiming at recognizing the information of human face expression, extracting the more important local feature for expression recognition, the technique of withinclass modular PCA was presented. The images were divided firstly and the PCA method was directly used to the subimages obtained from the previous step. Then the subspaces for each class of training samples were calculated. Finally, the distances from the tested samples to the subspace were computed and the classified recognition was carried by the nearestneighbor. The results of the experiment in the Japanese female face expression database indicate that the recognition rate of the modular PCA is obviously superior to that of traditional PCA.
Key words: principal component analysis (PCA); feature extraction; withinclass modular PCA; facial expression recognition (FER)
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