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In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show...
In this paper, a novel approximate solution of the criterion used in non-linear class-specific discriminant subspace learning is proposed. We build on the class-specific kernel spectral regression method, which is a two-step process formed by an eigenanalysis step and a kernel regression step. Based on the structure of the intra-class and out-of-class scatter matrices, we provide a fast solution for...
In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems,...
In this paper, we propose a new approach for nonlinear Class-specific Discriminant Analysis that exploits a class-specific kernel space definition. We show that the proposed method can considerably reduce the time and space complexities of the standard Class-specific Kernel Discriminant Analysis. Our analysis is verified by experiments illustrating the efficiency of the proposed class-specific kernel-based...
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