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Face recognition using a kernel fractional-step discriminant analysis algorithm

8 years 9 months ago
Face recognition using a kernel fractional-step discriminant analysis algorithm
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kernel fractional-step discriminant analysis (KFDA) for nonlinear feature extraction and dimensionality reduction. Not only can this new algorithm, like other kernel methods, deal with nonlinearity required for many face recognition tasks, it can also outperform traditional KDA algorithms in resisting the adverse effects due to outlier classes. Moreover, to further strengthen the overall performance of KDA algorithms for face recognition, we propose two new kernel functions: cosine fractional-power polynomial kernel and non-normal Gaussian RBF kernel. We perform extensive comparative studies based on the YaleB and FERET face databases. Experimental results show that our KFDA algorithm outperforms traditional kernel principal component analysis (KPCA) and KDA algorithms. Moreover, further improvement can be obtain...
Guang Dai, Dit-Yan Yeung, Yuntao Qian
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PR
Authors Guang Dai, Dit-Yan Yeung, Yuntao Qian
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