Kernel relevance weighted discriminant analysis for face recognition
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In this paper, we propose a new kernel discriminant analysis called kernel relevance weighted discriminant analysis (KRWDA) which has several interesting characteristics. First, it can effectively deal with the small sample size problem by using a QR decomposition on scatter matrices. Second, by incorporating a weighting function into discriminant criterion, it overcomes overemphasis on well-separated classes and hence can work under more realistic situations. Finally, using kernel theory, it handle non linearity efficiently. In order to improve performance of the proposed algorithm, we introduce two novel kernel functions and compare them with some commonly used kernels on face recognition field. We have performed multiple face recognition experiments to compare KRWDA with other dimensionality reduction methods showing that KRWDA consistently gives the best results.
KeywordsKernel discriminant analysis RWLDA Kernel functions Small sample Size problem Face recognition
The authors wish to thank the anonymous reviewers, whose comments were a great help to raise the level of technical and formal correctness of this paper.
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