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A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first...
Multimodal images (for example, optical image, MR, mammography) are widely used in many practical areas, for example, face recognition, image retrieval, and medical assisted diagnosis. In this paper, we proposed a novel image recognition method of kernel common discriminant based image classification. Firstly, we analyze the limitations of the traditional discriminative common vector (DCV) on the...
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 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;...
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...
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