  Person re-identification is the task of matching pedestrian images across
non-overlapping cameras. In this paper, we propose a non-linear cross-view
similarity metric learning for handling small size training data in practical
re-ID systems. The method employs non-linear mappings combined with cross-view
discriminative subspace learning and cross-view distance metric learning based
on pairwise similarity constraints. It is a natural extension of XQDA from
linear to non-linear mappings using kernels, and learns non-linear
transformations for efficiently handling complex non-linearity of person
appearance across camera views. Importantly, the proposed method is very
computationally efficient. Extensive experiments on four challenging datasets
shows that our method attains competitive performance against state-of-the-art
methods.
