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The paper proposes a fuzzy face recognition system based on the integration of principal component analysis (PCA) and linear discriminant analysis (LDA). It aims to find out the eigenvalues, eigenvectors, and eigenspace of human facial features using PCA firstly, and then obtain the data of facial weightings by projecting the eigenvalues to eigenspace of human face. The purposes of integrating LDA...
We propose a subspace based method for quantitative analysis of facial appearance. We first collect both natural and cosmetic female facial images and then construct a subspace of female facial images using principal component analysis (PCA). We divide the subspace into two subspaces: one is global subspace which is constructed by eigenvectors with larger eigenvalues and another one is local subspace...
Face recognition is one of the most fundamental functions for many kinds of intelligent robots. Among many methods proposed for face recognition, linear approaches such as Eigenface or principal component analysis (PCA), Fisher's linear discriminant (FLD), Fisherface, and common vector (CV), have attracted great attention because of their simplicity. It is known that when the number of training examples...
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