  Most person re-identification (ReID) approaches assume that person images are
captured under relatively similar illumination conditions. In reality,
long-term person retrieval is common, and person images are often captured
under different illumination conditions at different times across a day. In
this situation, the performances of existing ReID models often degrade
dramatically. This paper addresses the ReID problem with illumination
variations and names it as {\em Illumination-Adaptive Person Re-identification
(IA-ReID)}. We propose an Illumination-Identity Disentanglement (IID) network
to dispel different scales of illuminations away while preserving individuals'
identity information. To demonstrate the illumination issue and to evaluate our
model, we construct two large-scale simulated datasets with a wide range of
illumination variations. Experimental results on the simulated datasets and
real-world images demonstrate the effectiveness of the proposed framework.
