  Person re-identification addresses the problem of matching pedestrian images
across disjoint camera views. Design of feature descriptor and distance metric
learning are the two fundamental tasks in person re-identification. In this
paper, we propose a metric learning framework for person re-identification,
where the discriminative metric space is learned using Kernel Fisher
Discriminant Analysis (KFDA), to simultaneously maximize the inter-class
variance as well as minimize the intra-class variance. We derive a Mahalanobis
metric induced by KFDA and argue that KFDA is efficient to be applied for
metric learning in person re-identification. We also show how the efficiency of
KFDA in metric learning can be further enhanced for person re-identification by
using two simple yet efficient multiple kernel learning methods. We conduct
extensive experiments on three benchmark datasets for person re-identification
and demonstrate that the proposed approaches have competitive performance with
state-of-the-art methods.
