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Appearance of individuals across multiple cameras varies a lot due to illumination and viewpoint changes making person re-identification a challenging problem. In this paper, we describe how to model this appearance variation by using a novel Weighted Brightness Transfer Function (WBTF). In combination with powerful low-level features, we show that WBTF leads to large performance improvements by assigning different weights to different BTFs and combining them accordingly. We have compared our algorithm on two public benhmark datasets: VIPeR and CAVIAR4REID dataset, achieving new state-of-the art performance on both datasets.