Texton Correlation for Recognition

We study the problem of object, in particular face, recognition under varying imaging conditions. Objects are represented using local characteristic features called textons. Appearance variations due to changing conditions are encoded by the correlations between the textons. We propose two solutions to model these correlations. The first one assumes locational independence. We call it the conditional texton distribution model. The second captures the second order variations across locations using Fisher linear discriminant analysis. We call it the Fisher texton model. Our two models are effective in the problem of face recognition from a single image across a wide range of illuminations, poses, and time.

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