  In this paper, we address the problem of person re-identification, which
refers to associating the persons captured from different cameras. We propose a
simple yet effective human part-aligned representation for handling the body
part misalignment problem. Our approach decomposes the human body into regions
(parts) which are discriminative for person matching, accordingly computes the
representations over the regions, and aggregates the similarities computed
between the corresponding regions of a pair of probe and gallery images as the
overall matching score. Our formulation, inspired by attention models, is a
deep neural network modeling the three steps together, which is learnt through
minimizing the triplet loss function without requiring body part labeling
information. Unlike most existing deep learning algorithms that learn a global
or spatial partition-based local representation, our approach performs human
body partition, and thus is more robust to pose changes and various human
spatial distributions in the person bounding box. Our approach shows
state-of-the-art results over standard datasets, Market-$1501$, CUHK$03$,
CUHK$01$ and VIPeR.
