  Person re-identification aims at matching pedestrians observed from
non-overlapping camera views. Feature descriptor and metric learning are two
significant problems in person re-identification. A discriminative metric
learning method should be capable of exploiting complex nonlinear
transformations due to the large variations in feature space. In this paper, we
propose a nonlinear local metric learning (NLML) method to improve the
state-of-the-art performance of person re-identification on public datasets.
Motivated by the fact that local metric learning has been introduced to handle
the data which varies locally and deep neural network has presented outstanding
capability in exploiting the nonlinearity of samples, we utilize the merits of
both local metric learning and deep neural network to learn multiple sets of
nonlinear transformations. By enforcing a margin between the distances of
positive pedestrian image pairs and distances of negative pairs in the
transformed feature subspace, discriminative information can be effectively
exploited in the developed neural networks. Our experiments show that the
proposed NLML method achieves the state-of-the-art results on the widely used
VIPeR, GRID, and CUHK 01 datasets.
