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In the sparse representation of a target sample, most nonzero coefficients belong to the neighbors of the target sample. Combining this observation with the theory of manifold learning, we propose a novel unsupervised feature extraction approach named local sparsity preserving projection (LSPP). LSPP sparsely reconstructs a target training sample from merely its neighbors, and seeks a subspace where...
Typically, two aspects are used to evaluate the quality of a classification model, i.e., the classifying accuracy and the interpretability. The recently developed sparse representation-based face recognition techniques, though achieving high accuracies, rarely concern the interpretability of the classification model. In particular, the obtained sparseness, in terms of the sparse representative coefficient...
The key of color face recognition technique is how to effectively utilize the complementary information between color components and remove their redundancy. Present color face recognition methods generally reduce the correlations between color components in the image pixel level, and then extract the discriminant features from the uncorrelated color face images. In this paper, we propose a novel...
Sparse representation has been extensively studied in the signal processing community, which surprisingly pointed out that one target signal can be accurately represented as a linear combination of very few measurement signals, often called atoms, in a given dictionary. This discovery has soon been employed to the field of pattern recognition and more recently, given rise to a newly developed unsupervised...
Fourier transform is a widely used image processing technology. Kernel discriminant analysis is an effective nonlinear feature extraction technique. Based on them, we propose a novel feature extraction approach for face recognition. First, we perform the Fourier transform on face images and express the Fourier frequency bands in the plural form. By computing the kernel-plural discriminant capability...
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...
In this paper, a novel Gabor-2DFisherface approach with selecting 2D Gabor principal components and discriminant vectors is proposed for face recognition. Gabor transform is an important frequency-domain analysis tool. The proposed approach combines it with discriminant analysis technique. This approach first preprocesses all image samples by using Gabor transform, and then calculates 2D Gabor principal...
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