Lambert an model is a classical illumination model consisting of a surface albe do component and a light intensity component. Some previous researches assume that the light intensity component mainly lies in the large-scale features.

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MULTISCALE LOGARITHM DIFFERENCE EDGEMAPS FOR FACE
RECOGNITION AGAINST VARYING LIGHTING CONDITIONS
ABSTRACT
Lambert an model is a classical illumination model consisting of a surface albe do
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 alogarithm transform, which can
change the multiplication of surface albe do and light intensity into an additive model. Then, a
difference (subs traction) between two pixels in a neighborhood can eliminate most of the light
intensity component. By dividing neighborhood into sub-regions, edge-maps of multiple scales
can be obtained. Then, each edge-map is multiplied by a weight that can be determined by an
independent training scheme. Finally, all the weighted edge-maps are combined to form a robust
holistic feature map. Extensive experiments on four benchmark data set sin controlled and
uncontrolled lighting conditions show that the proposed method has promising results, especially
in uncontrolled lighting conditions, even mixed with other complicated variations.