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Two-dimensional principal component analysis (2DPCA) and two-dimensional linear discriminant analysis (2DLDA) are new techniques for face recognition. The main ideas behind 2DPCA and 2DLDA are that they are based on 2D matrices as opposed to the traditional PCA and LDA, which are based on 1D vector. In some literature, there has been a tendency to prefer 2DLDA over 2DPCA because, as intuition would...
An effective face recognition method is described in the proposed paper, which is based on Gabor Wavelets and 2D Linear Discriminant Analysis (Gabor-2DLDA). Although Gabor features has been recognized as one of the most successful face representations, its huge number of features often brings about the problem of curse of dimensionality. In this paper, we use Gabor feature matrix to represent the...
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