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Extracting invariable features is one key issue for 3D model searching. A novel invariable feature extraction method, namely geometry projection based histogram model, is proposed for 3D model description. Different from the traditional method, one projection plane (or surface) is created for each 3D model, and the points of 3D models are projected to the projection plane (or surface), and then the...
In the face recognition area, a so-called one sample per person problem occurred owing to the difficulties of collecting samples or storage space of systems. In this paper, we present a unified framework for image matrix based face recognition with one training sample per person. Firstly, the nonlinear and linear facial features are using proposed 2DKPCA and 2D(PC)2A method, the face images are directly...
In the real-world application of face recognition system, owing to the difficulties of collecting samples or storage space of systems, only one sample image per person is stored in the system, which is so-called one sample per person problem. In this paper, we propose a novel algorithm, called 2D(PC)2A, to solve this problem. The procedure of 2D(PC)2A can be divided into the three stages: 1) creating...
A novel feature extraction method, namely Laplacian discriminant projection with optimized kernels (KLDP-Opt) algorithm is proposed in this paper. The advantage of KLDP-Opt lies in: 1) the similarity matrix is constructed with the class-wise nonparametric similarity measure where it solves procedure selection problem; 2) data-dependent kernel is applied to solve the limitation of linearity of LPP,...
A novel face recognition method based on facial texture feature with common vector analysis is presented in this paper. The novelty of this paper comes from (1) facial texture feature characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations in illumination and facial expressions is extracted by Gabor wavelet, which improves the recognition performance;...
An efficient reformative kernel discriminant analysis, namely enhanced kernel discriminant analysis (EKDA), is proposed in this paper. In the proposed algorithm, a novel criterion, i.e., maximizing the class separability both in the feature space and in the projection subspace, is presented to enhance the discriminant power of KDA. EKDA is more adaptive to the input data under the novel criterion...
An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the...
Gaussian kernel is widely used in Support Vector Machines and many other kernel methods, and it is most often deemed to provide a local measure of similarity between vectors, which causes large storage requirements and large computational effort for transforming images to vectors owing to its viewing images as vectors. A novel matrix norm based Gaussian kernel (M-Gaussian kernel) which views images...
Subspace analysis is an effective technique for feature extraction, which aims at finding a low-dimensional space of high-dimensional data. In this paper, a novel subspace analysis method based on data-dependent kernel discriminant analysis (DDKDA) is proposed for dimension reduction. The procedure of DDKDA contains two stages: one is to find the optimal combination coefficients by solving a constrained...
This paper presents a novel face recognition method based on complete Kernel Fisher discriminant (CKFD) analysis of Gabor features with power polynomial models. By integrating the Gabor wavelet representation of face images and the enhanced powerful discriminator named CKFD analysis, the method is robust to changes in illumination and facial expressions and poses. On the other hand, the extended polynomial...
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