MULTISCALE LOGARITHM DIFFERENCE EDGEMAPS FOR FACE RECOGNITION AGAINST VARYING LIGHTING CONDITIONS

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MULTISCALE LOGARITHM DIFFERENCE EDGEMAPS FOR FACE
RECOGNITION AGAINST VARYING LIGHTING CONDITIONS
ABSTRACT
Lambertian model is a classical illumination model consisting of a surface albedo
component and a light intensity component. Some previous researches assume that the light
intensity component mainly lies in the large-scale features. They adopt holistic image
decompositions to separate it out, but it is difficult to decide the separating point between large-
scale and small-scale features. In this paper, we propose to take a logarithm transform, which can
change the multiplication of surface albedo and light intensity into an additive model. Then, a
difference (substraction) between two pixels in a neighborhood can eliminate most of the light
intensity component. By dividing a neighborhood into subregions, edgemaps of multiple scales
can be obtained. Then, each edgemap is multiplied by a weight that can be determined by an
independent training scheme. Finally, all the weighted edgemaps are combined to form a robust
holistic feature map. Extensive experiments on four benchmark data sets in controlled and
uncontrolled lighting conditions show that the proposed method has promising results, especially
in uncontrolled lighting conditions, even mixed with other complicated variations.

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MULTISCALE LOGARITHM DIFFERENCE EDGEMAPS FOR FACE RECOGNITION AGAINST VARYING LIGHTING CONDITIONS

1. MULTISCALE LOGARITHM DIFFERENCE EDGEMAPS FOR FACE RECOGNITION AGAINST VARYING LIGHTING CONDITIONS ABSTRACT Lambertian model is a classical illumination model consisting of a surface albedo component and a light intensity component. Some previous researches assume that the light intensity component mainly lies in the large-scale features. They adopt holistic image decompositions to separate it out, but it is difficult to decide the separating point between large- scale and small-scale features. In this paper, we propose to take a logarithm transform, which can change the multiplication of surface albedo and light intensity into an additive model. Then, a difference (substraction) between two pixels in a neighborhood can eliminate most of the light intensity component. By dividing a neighborhood into subregions, edgemaps of multiple scales can be obtained. Then, each edgemap is multiplied by a weight that can be determined by an independent training scheme. Finally, all the weighted edgemaps are combined to form a robust holistic feature map. Extensive experiments on four benchmark data sets in controlled and uncontrolled lighting conditions show that the proposed method has promising results, especially in uncontrolled lighting conditions, even mixed with other complicated variations.

MULTISCALE LOGARITHM DIFFERENCE EDGEMAPS FOR FACE RECOGNITION AGAINST VARYING LIGHTING CONDITIONS

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