Kernel discriminant analysis (KDA) method is a promising approach for non-linear feature extraction in face identification tasks. However, as a linear algorithm to address nonlinear problem, Fisher discriminant analysis (FDA) approach will not give a satisfactory performance. Moreover, FDA usually suffers from small sample size (S3) problem. To overcome these two shortcomings in FDA method, Shannon wavelet kernel based subspace FDA (SKDA) algorithm is developed in this paper. Two public databases such as FERET and CMU PIE databases are selected for evaluation. Comparing with the existing kernel based FDA-based methods, the proposed method gives superior results. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Chen, W. S., Yuen, P. C., & Lai, J. H. (2007). Subspace KDA algorithm for non-linear feature extraction in face identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4456 LNAI, pp. 1106–1114). Springer Verlag. https://doi.org/10.1007/978-3-540-74377-4_116
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