基于分块的2DPCA人脸识别方法  

A M-2DPCA Face Recognition Method

作　　者：李靖平[1] 

机构地区：[1]黎明职业大学,福建泉州362000

出　　处：《浙江万里学院学报》2014年第2期93-98,97,共6页Journal of Zhejiang Wanli University

基　　金：福建省教育厅B类科技研究项目（JB12487S）;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）.

摘　　要：文章将分块理论与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。结论 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。Aim： The block theory and two-dimensional principal component analysis （2DPCA） were combined, and the modular two-dimensional principal component analysis （M-2DPCA） was studied in face recognition. Methods： The original image matrix was divided into modular image matrixes , and the image covariance matrix was formed directly by using sub-image matrixes , and its eigenvectors were derived. The eigenvectors were used to extract and analyze image feature for face recognition. Results：The Experiments based on the Yale face database showed that it had a higher recognition rate of M-2DPCA than 2DPCA, under the same training specimens and eigenvectors. Conclusion： 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.

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) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]