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- W2363647738 abstract "The classical principal component analysis (PCA) for image feature extraction is usually based on vectors, which make 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, in this paper, a generalized K-L transformation is developed for image feature extraction. In this method, based on original image matrices directly, the discriminant information included in the class mean images is optimally compressed. Experimental results on ORL face database show the proposed method are more powerful and efficient than the classical PCA and Fisher linear discriminant analysis." @default.
- W2363647738 created "2016-06-24" @default.
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- W2363647738 date "2003-01-01" @default.
- W2363647738 modified "2023-09-25" @default.
- W2363647738 title "A Generalized K-L Transformation and Face Recognition" @default.
- W2363647738 hasPublicationYear "2003" @default.
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