  Most existing person re-identification (re-id) methods are unsuitable for
real-world deployment due to two reasons: Unscalability to large population
size, and Inadaptability over time. In this work, we present a unified solution
to address both problems. Specifically, we propose to construct an Identity
Regression Space (IRS) based on embedding different training person identities
(classes) and formulate re-id as a regression problem solved by identity
regression in the IRS. The IRS approach is characterised by a closed-form
solution with high learning efficiency and an inherent incremental learning
capability with human-in-the-loop. Extensive experiments on four benchmarking
datasets(VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not
only outperforms state-of-the-art re-id methods, but also is more scalable to
large re-id population size by rapidly updating model and actively selecting
informative samples with reduced human labelling effort.
