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This paper proposes a theorem on the uncorrelated optimal discriminant vectors (UODVs). It is proved that the classical optimal discriminant vectors are equivalent to UODV, which can be used to extract (L-1) uncorrelated discriminant features for L-class problems without losing any discriminant information in the meaning of Fisher discriminant criterion function. Experiments on Concordia University...
The extraction of discriminant features is the most fundamental and important problem in face recognition. This paper presents a method to extract optimal discriminant features for face images by using the uncorrelated discriminant transformation and KL expansion. Experiments on the ORL database and the NUST603 database have been performed. Experimental results show that the uncorrelated discriminant...
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