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| Correlation Minimized Based 2-Dimensional Principal Component Analysis |
| YAN Hui,JIN Zhong,YANG Jing-Yu |
| School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 |
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Abstract It is indicated that two components belonging to the feature vector are correlated and the corresponding mathematical expression (2DPCA) is presented. The correlation minimized based 2-dimensional principal component analysis is proposed. It maximizes the total scatter of the feature vectors meanwhile minimizes the correlations of arbitrary two components belonging to the feature vector. The experimental results on Yale face database indicate that the proposed method has powerful ability of feature extraction and higher face recognition rates than 2DPCA and DiaPCA.
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Received: 17 November 2008
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2010, Vol. 23 
