  Person re-identification (re-id) is the task of matching multiple occurrences
of the same person from different cameras, poses, lighting conditions, and a
multitude of other factors which alter the visual appearance. Typically, this
is achieved by learning either optimal features or matching metrics which are
adapted to specific pairs of camera views dictated by the pairwise labelled
training datasets. In this work, we formulate a deep learning based novel
approach to automatic prototype-domain discovery for domain perceptive
(adaptive) person re-id (rather than camera pair specific learning) for any
camera views scalable to new unseen scenes without training data. We learn a
separate re-id model for each of the discovered prototype-domains and during
model deployment, use the person probe image to select automatically the model
of the closest prototype domain. Our approach requires neither supervised nor
unsupervised domain adaptation learning, i.e. no data available from the target
domains. We evaluate extensively our model under realistic re-id conditions
using automatically detected bounding boxes with low-resolution and partial
occlusion. We show that our approach outperforms most of the state-of-the-art
supervised and unsupervised methods on the latest CUHK-SYSU and PRW benchmarks.
