基于分块的2DPCA人脸识别方法 |
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引用本文: | 王小欧. 基于分块的2DPCA人脸识别方法[J]. 长春师范学院学报, 2014, 0(1): 40-44 |
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作者姓名: | 王小欧 |
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作者单位: | 黎明职业大学信息与电子工程学院,福建泉州362000 |
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基金项目: | 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). |
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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...
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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 |
A M-2DPCA Face Recognition Method |
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Affiliation: | LI Jing - ping (Liming Vocational University, Quanzhou Fujian 362000,China) |
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Abstract: | 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. |
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Keywords: | Two - Dimensional Principal Component Analysis (2DPCA) M - 2DPCA Feature Extraction Face Recognition |
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