  Person re-identification aims at the maintenance of a global identity as a
person moves among non-overlapping surveillance cameras. It is a hard task due
to different illumination conditions, viewpoints and the small number of
annotated individuals from each pair of cameras (small-sample-size problem).
Collaborative Representation based Classification (CRC) has been employed
successfully to address the small-sample-size problem in computer vision.
However, the original CRC formulation is not well-suited for person
re-identification since it does not consider that probe and gallery samples are
from different cameras. Furthermore, it is a linear model, while appearance
changes caused by different camera conditions indicate a strong nonlinear
transition between cameras. To overcome such limitations, we propose the Kernel
Cross-View Collaborative Representation based Classification (Kernel X-CRC)
that represents probe and gallery images by balancing representativeness and
similarity nonlinearly. It assumes that a probe and its corresponding gallery
image are represented with similar coding vectors using individuals from the
training set. Experimental results demonstrate that our assumption is true when
using a high-dimensional feature vector and becomes more compelling when
dealing with a low-dimensional and discriminative representation computed using
a common subspace learning method. We achieve state-of-the-art for rank-1
matching rates in two person re-identification datasets (PRID450S and GRID) and
the second best results on VIPeR and CUHK01 datasets.
