摘要
将分块理论与2DPCA方法相结合,研究分块二维主成分分析法（M-2DPCA）在人脸识别中的应用.对人脸图像矩阵进行分块,用形成的子图像矩阵直接构造总体散布矩阵并求解对应的特征向量,利用提取的特征向量对图像进行特征的提取与分析,进行人脸识别.基于Yale人脸数据库的实验显示,在相同训练样本和特征向量条件下,M-2DPCA比2DPCA算法具有更高的识别率.M-2DPCA充分利用了图像的协方差信息,在人脸识别方面具有较高的识别率和鲁棒性方面,对进一步研究人脸识别具有重要的意义.
The block theory and two- dimensional principal component analysis （2DPCA） were combined, and the modular two- di- mensional principal component analysis （M -2DPCA） was studied in face recognition. The original image matrix was divided into modu- lar image matrixes, and the image covariance matrix was formed directly by using sub - image matrixes, and its eigenvectors were de- rived. The eigenvectors were used to extract and analyze image feature for face recognition. The experiments based on the Yale face data- base showed that it had a higher recognition rate of M -2DPCA than 2DPCA under the same training specimens and eigenvectors. The information of image covariance matrix was fully utilized in M - 2DPCA method, which had an admirable recognition rate and robustness on face recognition, and it was important to further research on face recognition.
作者
李靖平
LI Jing - ping （Liming Vocational University, Quanzhou Fujian 362000,China）
基金
福建省教育厅B类科技研究项目（JBl2487S）
泉州市技术研究与开发项目高校协同创新科技项目（20122131）
泉州市科技局科技资助项目（2008G16）.
关键词
二维主成分分析
分块二维主成分分析法
特征提取
人脸识别
Two - Dimensional Principal Component Analysis （2DPCA）
M - 2DPCA
Feature Extraction
Face Recognition
