Zeng ZWang ZWang ZZheng YChuang Y.-YSatoh S.YUNG-YU CHUANG2021-09-022021-09-02202015209210https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084918274&doi=10.1109%2fTMM.2020.2969782&partnerID=40&md5=18843e630f60a527985365e8238641f5https://scholars.lib.ntu.edu.tw/handle/123456789/581485Most 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 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. ? 1999-2012 IEEE.Multimedia systems; Signal processing; Identity information; Illumination conditions; Illumination variation; Person re identifications; Real-world image; Simulated datasets; Large dataset[SDGs]SDG11journal article10.1109/TMM.2020.29697822-s2.0-85084918274