  Person images captured by surveillance cameras are often occluded by various
obstacles, which lead to defective feature representation and harm person
re-identification (Re-ID) performance. To tackle this challenge, we propose to
reconstruct the feature representation of occluded parts by fully exploiting
the information of its neighborhood in a gallery image set. Specifically, we
first introduce a visible part-based feature by body mask for each person
image. Then we identify its neighboring samples using the visible features and
reconstruct the representation of the full body by an outlier-removable graph
neural network with all the neighboring samples as input. Extensive experiments
show that the proposed approach obtains significant improvements. In the
large-scale Occluded-DukeMTMC benchmark, our approach achieves 64.2% mAP and
67.6% rank-1 accuracy which outperforms the state-of-the-art approaches by
large margins, i.e.,20.4% and 12.5%, respectively, indicating the effectiveness
of our method on occluded Re-ID problem.
