双向压缩二维特征抽取人脸识别新方法  

New Method of Tow Direction and Two Dimension Extract Features for Face Recognition

作　　者：郭志强[1] 杨杰[1] 

机构地区：[1]武汉理工大学信息工程学院,武汉430070

出　　处：《计算机科学》2009年第11期296-299,共4页Computer Science

基　　金：国家自然科学基金(50775167);湖北省科技攻关项目(2007A101c52)资助

摘　　要：提出了二维主成分分析(2DPCA)与二维线性鉴别分析(2DLDA)相结合的双向压缩投影的子空间人脸识别方法。该方法在进行一次2DPCA运算后,对特征矩阵进行转置,再进行2DLDA运算,与(2D)2PCA与(2D)2LDA相比,充分利用了2DPCA和2DLDA的优点,既包含了样本的类别信息,又消除了图像矩阵行和列的相关性,有效地提取了行和列的识别信息,识别特征维数也大幅度减少。在ORL和PERET人脸库上的实验表明,在不影响识别速度的情况下,其识别率优于现有二维特征提取方法。Two-way compression project subspace method combining both the Two-dimension Principle Component Analysis （2DPCA） and the Two-dimension Linear Discriminant Analysis （2DLDA） was proposed for face recognition. This method first transposes the feature matrix after it performs the 2DPCA and then it performs the 2DLDA. Compared with the （2D）2 PCA and （2D）2 LDA, this method makes full use of the advantages of the 2DPCA and 2DLDA. It not only contains the sample category information, but also eliminates the image matrix correlation of the row and column, so that it effectively extracts the row and column recognition information, and meanwhile, the recognition ieature dimension decreases dramatically. The experiment on the ORL and PERET face databases shows that the recognition rate of this method is better than the existing two-dimension feature extract method without influencing the recognition speed.

关 键 词：人脸识别 二维线性鉴别分析 二维主成分分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]