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In this paper, we propose a novel kernel discriminant feature extraction framework based on the mapped virtual samples (MVS) for face recognition. We calculate a non-symmetric kernel matrix by constructing a few virtual samples (including eigen-samples and common vector samples) in the input space, and then express kernel projection vectors by using mapped virtual samples (MVS). Under this framework,...
In this paper, a kernel uncorrelated adjacent-class discriminant analysis (KUADA) approach is proposed for image recognition. The optimal nonlinear discriminant vector obtained by this approach can differentiate one class and its adjacent classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices in kernel space using the Fisher criterion...
In this paper, a novel discriminant analysis approach using Angular Fourier transform is proposed for face recognition. As a generalization of Fourier transform, the Angular Fourier transform is an important frequency-domain analysis technique. The proposed approach combines it with discriminant analysis method. First, this approach selects appropriate value of angle parameter for discrete Angular...
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