文章基本信息

标题：Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition
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作者：Xiao-Zhang Liu ; Guo-Can Feng
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2013
卷号：10
DOI：10.5772/52350
语种：English
出版社：SAGE Publications
摘要：Recent applications and developments based on support vector machines (SVMs) have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel-based Fisher discriminant analysis (kernel-based FDA) method with multiple kernels. This paper proposes a multiple kernel construction method for kernel-based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC), we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD) achieves high recognition performance, compared with single-kernel-based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel-based FDA to some extent.
关键词：Multiple Kernel Learning (MKL); Kernel-based Fisher Discriminant Analysis (kernel-based FDA); Margin Maximization Criterion (MMC); Weight Optimization