Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition

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.