Perception-based lighting adjustment of image sequences

In this paper, we propose a 2-step algorithm to reduce the lighting influences between frames in an image sequence. First, the lighting parameters of a perceptual lighting model are initialized using an entropy measure. Then the difference between two successive frames is used as a cost function for further optimization the above lighting parameters. By applying the proposed lighting model optimization on an image sequence, the neighboring frames become similar in brightness and contrast while features are enhanced. The effectiveness of the proposed approach is illustrated on the detection and tracking of facial features.