principal component analysis (PCA ) is a well known approach for dimensionality reduction of the feature space. It has been successfully applied in face recognition. The main idea is to decompose face images into a small set of feature images called eigenfaces, which can be considered as points in a linear subspace called “face space” or “eigenspace”

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