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- W2389315092 abstract "Aiming at the problem that Kernel Principal Component Analysis(KPCA) can not effectively use the feature of face symmetry,and generally lacks of training samples in face recognition,so the recognition rate is low.Therefore,this paper proposes a Symmetrical Kernel Principal Component Analysis(SKPCA) algorithm.This algorithm fully utilizes the face mirror symmetry,the odd symmetry samples and the even symmetry samples are received by mirror transforming for training samples.Odd/even symmetrical principal components are respectively extracted.A nearest neighbor classifier is employed to classify the extracted features.Experimental results show that this algorithm enlarges the number of training samples,when the polynomial order number is 2,the recognition rate of this algorithm is better than that of the KPCA algorithm,and recognition time is shorter than KPCA algorithm." @default.
- W2389315092 created "2016-06-24" @default.
- W2389315092 creator A5068683159 @default.
- W2389315092 date "2013-01-01" @default.
- W2389315092 modified "2023-09-23" @default.
- W2389315092 title "Symmetric Kernel Principal Component Analysis and Its Application in Face Recognition" @default.
- W2389315092 hasPublicationYear "2013" @default.
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