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Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study,...
Many researchers exert to find the best discriminant transformation in eigen spaces to reduce the facial pose, illumination, and expression (PIE) impacts for obtaining the better recognition results. Covariance matrix which represents dimensional correlation among samples plays the key role in projection-based methods for face recognition. In this study, a mixture of nearest feature points (NFP) and...
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