  Person attributes are often exploited as mid-level human semantic information
to help promote the performance of person re-identification task. In this
paper, unlike most existing methods simply taking attribute learning as a
classification problem, we perform it in a different way with the motivation
that attributes are related to specific local regions, which refers to the
perceptual ability of attributes. We utilize the process of attribute detection
to generate corresponding attribute-part detectors, whose invariance to many
influences like poses and camera views can be guaranteed. With detected local
part regions, our model extracts local features to handle the body part
misalignment problem, which is another major challenge for person
re-identification. The local descriptors are further refined by fused attribute
information to eliminate interferences caused by detection deviation. Extensive
experiments on two popular benchmarks with attribute annotations demonstrate
the effectiveness of our model and competitive performance compared with
state-of-the-art algorithms.
