Double Discriminant Analysis for Face Recognition

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Executive Summary

Feature selection for face representation is one of the central issues for any face recognition system. Finding a lower dimensional feature space with enhanced discriminating power is one of the important tasks. The traditional subspace methods represent each face image as a point in the disciminant subspace that is shared by all faces of different subject (classes). Such type of representation fails to accurately represent the most discriminate features related to one class of face, so in order to extract features that capture a particular class's notion of similarity and differ much from remaining classes is modeled.

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