  In video surveillance, person re-identification is the task of searching
person images in non-overlapping cameras. Though supervised methods for person
re-identification have attained impressive performance, obtaining large scale
cross-view labeled training data is very expensive. However, unlabelled data is
available in abundance. In this paper, we propose a semi-supervised metric
learning approach that can utilize information in unlabelled data with the help
of a few labelled training samples. We also address the small sample size
problem that inherently occurs due to the few labeled training data. Our method
learns a discriminative space where within class samples collapse to singular
points, achieving the least within class variance, and then use a maximum
margin criterion over a high dimensional kernel space to maximally separate the
distinct class samples. A maximum margin criterion with two levels of high
dimensional mappings to kernel space is used to obtain better cross-view
discrimination of the identities. Cross-view affinity learning with reciprocal
nearest neighbor constraints is used to mine new pseudo-classes from the
unlabelled data and update the distance metric iteratively. We attain
state-of-the-art performance on four challenging datasets with a large margin.
