  Most existing works in Person Re-identification (ReID) focus on settings
where illumination either is kept the same or has very little fluctuation.
However, the changes in the illumination degree may affect the robustness of a
ReID algorithm significantly. To address this problem, we proposed a Two-Stream
Network that can separate ReID features from lighting features to enhance ReID
performance. Its innovations are threefold: (1) A discriminative entropy loss
to ensure the ReID features contain no lighting information. (2) A ReID Teacher
model trained by images under "neutral" lighting conditions to guide ReID
classification. (3) An illumination Teacher model trained by the differences
between the illumination-adjusted and original images to guide illumination
classification. We construct two augmented datasets by synthetically changing a
set of predefined lighting conditions in two of the most popular ReID
benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our
algorithm outperforms other state-of-the-art works and particularly potent in
handling images under extremely low light.
