  Person re-identification (ReID) is now an active research topic for AI-based
video surveillance applications such as specific person search, but the
practical issue that the target person(s) may change clothes (clothes
inconsistency problem) has been overlooked for long. For the first time, this
paper systematically studies this problem. We first overcome the difficulty of
lack of suitable dataset, by collecting a small yet representative real dataset
for testing whilst building a large realistic synthetic dataset for training
and deeper studies. Facilitated by our new datasets, we are able to conduct
various interesting new experiments for studying the influence of clothes
inconsistency. We find that changing clothes makes ReID a much harder problem
in the sense of bringing difficulties to learning effective representations and
also challenges the generalization ability of previous ReID models to identify
persons with unseen (new) clothes. Representative existing ReID models are
adopted to show informative results on such a challenging setting, and we also
provide some preliminary efforts on improving the robustness of existing models
on handling the clothes inconsistency issue in the data. We believe that this
study can be inspiring and helpful for encouraging more researches in this
direction. The dataset is available on the project website:
https://wanfb.github.io/dataset.html.
