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The conventional kernel PCA does not really nonlinearly maps an input image into a high-dimensional feature space. Rather, it chooses a kernel function a priori and computes the principal components indirectly within the input space spanned by the image pixels. Thus method does not consider the structural information of the input images in the feature space. Therefore, the computed principal components...
In this paper, an efficient method for face recognition using principal component analysis (PCA) and radial basis function (RBF) neural networks is presented. Recently, the PCA has been extensively employed for face recognition algorithms. It is one of the most popular representation methods for a face image. It not only reduces the dimensionality of the image, but also retains some of the variations...
This paper presents an approach to face recognition based on Dempster-Shafer (DS) theory of evidence, which combines the evidences of two radial basis function (RBF) neural networks. The degrees of belief of the two RBF neural networks for classification of an image have been estimated using two different feature vectors derived from images of the ORL face database. Then these degrees of belief have...
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