Abstract
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.
Highlights
As there exist many image variations such as pose, illumination and facial expression, face recognition is a highly complex and nonlinear problem which could not be sufficiently handled by linear methods, such as principal components analysis (PCA) [1] and Fisher discriminant analysis (FDA) [2]
We propose multiple kernel Fisher discriminant analysis (MKFD), in which the constructed kernel is a linear combination of several base kernels with a constraint on their weights, and we give an iterative scheme for weight optimization
To evaluate the performance of our MKFD for face recognition, we have made experimental comparisons with kernel direct FDA (KDDA) based on single kernels, in terms of low‐
Summary
As there exist many image variations such as pose, illumination and facial expression, face recognition is a highly complex and nonlinear problem which could not be sufficiently handled by linear methods, such as principal components analysis (PCA) [1] and Fisher discriminant analysis (FDA) [2]. 1u0o,-C1a4n2:F2e0n1g3: 1 Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition kernel parameters can not be guaranteed optimal. A single and fixed kernel can only characterize the geometrical structure of some aspects for the input data and, not always be fit for the applications which involve data from multiple, heterogeneous sources [9][10], such as face images under broad variations of pose, illumination, facial expression, aging, etc. MKL is proposed for SVMs, and there have been few reports on performance of the kernel‐based FDA method with multiple kernels. We propose multiple kernel Fisher discriminant analysis (MKFD), in which the constructed kernel is a linear combination of several base kernels with a constraint on their weights, and we give an iterative scheme for weight optimization.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
