Abstract
The feature selection is very important for improving classifier’s accuracy and reducing classifier’s running time. In this paper, a novel feature selection method based on KPCA, SVM and GSFS is proposed for face recognition. The proposed method can be described as follows, first KPCA is used for extracting initial face features, secondly, the extracted features are divided into some single feature sets, and then the single feature sets are trained separately by SVM to obtain the best feature set through GSFS. In this way, the dimensionality of the initial features can be reduced and also the best features can be obtained. Experimental results on ORL, IITL and UMIST face databases indicate the effectiveness of the proposed method.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, W., Gong, W., Liang, Y., Chen, W. (2005). Feature Selection Based on KPCA, SVM and GSFS for Face Recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_39
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DOI: https://doi.org/10.1007/11552499_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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