Abstract
In this paper, we propose a novel heteroscedastic weighted kernel discriminant analysis (HW-KDA) method that extends the linear discriminant analysis (LDA) to deal explicitly with heteroscedasticity and nonlinearity of the face pattern’s distribution by integrating the weighted pairwise Chernoff criterion and Kernel trick. The proposed algorithm has been tested, in terms of classification rate performance, on the multiview UMIST face database. Results indicate that the HW-KDA methodology is able to achieve excellent performance with only a very small set of features and outperforms other two popular kernel face recognition methods, the kernel PCA (KPCA) and generalized discriminant analysis (GDA).
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Liang, Y., Gong, W., Li, W., Pan, Y. (2005). Face Recognition Using Heteroscedastic Weighted Kernel Discriminant Analysis. 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_23
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DOI: https://doi.org/10.1007/11552499_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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