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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/2970
Title: Face recognition using a kernel fractional-step discriminant analysis algorithm
Authors: Dai, Guang
Yeung, Dit-Yan
Qian, Yun-Tao
Keywords: Face recognition
Feature extraction
Nonlinear dimensionality reduction
Kernel discriminant analysis
Kernel fractional-step discriminant analysis
Issue Date: 2006
Citation: Pattern recognition, v. 40, no. 1, January 2007, P. 229-243
Abstract: 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 obtained when the two new kernel functions are used.
Rights: Pattern Recognition © copyright (2006) Elsevier. The Journal's web site is located at http://www.sciencedirect.com/
URI: http://hdl.handle.net/1783.1/2970
Appears in Collections:CSE Journal/Magazine Articles

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