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| A Generalized Principal Component Analysis Based on Image Matrix |
| Chen Cai-kou①②; Yang Jian①; Yang Jing-yu①; Gao Xiu-mei① |
| ①Dept of Computer Science Nanjing Univ. of Sci. & Tech.,Nanjing 210094 China;②Dept of Computer Science Yangzhou University Yangzhou 225001 China |
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Abstract The classical Principal Component Analysis (PCA) for image feature extraction is usually based on vectors, which makes it very time-consuming, and the class information in the training sample has not been utilized fully also. To overcome these two drawbacks of PCA, this paper proposes a novel and efficient PCA method based on original image matri-ces directly. It can extract the discriminant information included in the class mean images. Hence, the proposed method has better discriminant performance than classical PCA. Ex-perimental results on ORL face database show the proposed method is more powerful and efficient than the classical PCA and Fisher linear discriminant analysis.
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Received: 21 July 2003
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2004, Vol. 26 
