  Person re-identification (re-ID) is a task of matching pedestrians under
disjoint camera views. To recognise paired snapshots, it has to cope with large
cross-view variations caused by the camera view shift. Supervised deep neural
networks are effective in producing a set of non-linear projections that can
transform cross-view images into a common feature space. However, they
typically impose a symmetric architecture, yielding the network ill-conditioned
on its optimisation. In this paper, we learn view-invariant subspace for person
re-ID, and its corresponding similarity metric using an adversarial view
adaptation approach. The main contribution is to learn coupled asymmetric
mappings regarding view characteristics which are adversarially trained to
address the view discrepancy by optimising the cross-entropy view confusion
objective. To determine the similarity value, the network is empowered with a
similarity discriminator to promote features that are highly discriminant in
distinguishing positive and negative pairs. The other contribution includes an
adaptive weighing on the most difficult samples to address the imbalance of
within/between-identity pairs. Our approach achieves notable improved
performance in comparison to state-of-the-arts on benchmark datasets.
