  Person re-identification (Re-ID) aims at retrieving an input person image
from a set of images captured by multiple cameras. Although recent Re-ID
methods have made great success, most of them extract features in terms of the
attributes of clothing (e.g., color, texture). However, it is common for people
to wear black clothes or be captured by surveillance systems in low light
illumination, in which cases the attributes of the clothing are severely
missing. We call this problem the Black Re-ID problem. To solve this problem,
rather than relying on the clothing information, we propose to exploit
head-shoulder features to assist person Re-ID. The head-shoulder adaptive
attention network (HAA) is proposed to learn the head-shoulder feature and an
innovative ensemble method is designed to enhance the generalization of our
model. Given the input person image, the ensemble method would focus on the
head-shoulder feature by assigning a larger weight if the individual insides
the image is in black clothing. Due to the lack of a suitable benchmark dataset
for studying the Black Re-ID problem, we also contribute the first Black-reID
dataset, which contains 1274 identities in training set. Extensive evaluations
on the Black-reID, Market1501 and DukeMTMC-reID datasets show that our model
achieves the best result compared with the state-of-the-art Re-ID methods on
both Black and conventional Re-ID problems. Furthermore, our method is also
proved to be effective in dealing with person Re-ID in similar clothing. Our
code and dataset are avaliable on https://github.com/xbq1994/.
