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In this paper, we first propose a method to transform from LDA to PCA with the discriminative information embedded in a whitening transformation, and then we propose a simple support vector machine formulation to LDA. The results of experiments of face recognition conducted on ORL database show the effectiveness of the proposed method.
Traditional planar shape representations cannot efficiently solve some problems such as recognition under occlusion, reconstruction and partial matching. In this paper, an improved shape representation by the extraction of contour curvature is presented based on the invariance of curvature. Then the invariance and discrete approximation solution are demonstrated. Finally the reconstruction method...
A system for the recognition and checkout of legal amounts on Chinese bank cheques was presented in the paper. Firstly, pianpang recognizer was proposed to combine any pianpang into a neighbor segment. Then, the width model of characters was proposed to determine the predicted positions of split lines. Finally, an efficient state transfer model was proposed to conduct Subset-based recognition. Experiments...
This paper puts forward to a new and valid Interpolationfaces method which combines interpolation method with Linear Discriminative Analysis (LDA) for face image recognition. After availability verification of this method, Comparison experiment of Interpolationfaces and Eigenfaces method which is based on Principle Component Analysis(PCA) dimension reduction is accomplished and better recognition...
As nonlinear feature extraction methods, kernel methods have been widely applied in pattern recognition. However, for high dimensional data such as face images, a kernel method corresponds to a high computational cost. In this paper, a novel idea and framework are presented to implement the kernel methods on high-dimensional data. A remarkable character of the framework is that there are two feature...
In KNRM (kernel-based nonlinear regression model) classifying for one test sample depends on all the kernel functions between each training sample and the test sample. As a result, the classification efficient is enslaved to the size of training set. In this paper KNRM is viewed as a ridge regression model with discrete outputs. Let it be supposed that in feature space discriminant vector can be approximated...
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