  Person retrieval faces many challenges including cluttered background,
appearance variations (e.g., illumination, pose, occlusion) among different
camera views and the similarity among different person's images. To address
these issues, we put forward a novel mask based deep ranking neural network
with a skipped fusing layer. Firstly, to alleviate the problem of cluttered
background, masked images with only the foreground regions are incorporated as
input in the proposed neural network. Secondly, to reduce the impact of the
appearance variations, the multi-layer fusion scheme is developed to obtain
more discriminative fine-grained information. Lastly, considering person
retrieval is a special image retrieval task, we propose a novel ranking loss to
optimize the whole network. The proposed ranking loss can further mitigate the
interference problem of similar negative samples when producing ranking
results. The extensive experiments validate the superiority of the proposed
method compared with the state-of-the-art methods on many benchmark datasets.
