Face recognition using a kernel fractional-step discriminant analysis algorithm

Guang Dai

doi:10.1016/j.patcog.2006.06.030

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Face recognition using a kernel fractional-step discriminant analysis algorithm

2007-01-01

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Guang Dai

钱

钱沄涛 (Yuntao Qian)

DOI：10.1016/j.patcog.2006.06.030

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Abstract

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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. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords:

face recognition

feature extraction

nonlinear dimensionality reduction

kernel discriminant analysis

kernel fractional-step discriminant analysis

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