  Person re-identification (Re-ID) aims to match identities across
non-overlapping camera views. Researchers have proposed many supervised Re-ID
models which require quantities of cross-view pairwise labelled data. This
limits their scalabilities to many applications where a large amount of data
from multiple disjoint camera views is available but unlabelled. Although some
unsupervised Re-ID models have been proposed to address the scalability
problem, they often suffer from the view-specific bias problem which is caused
by dramatic variances across different camera views, e.g., different
illumination, viewpoints and occlusion. The dramatic variances induce specific
feature distortions in different camera views, which can be very disturbing in
finding cross-view discriminative information for Re-ID in the unsupervised
scenarios, since no label information is available to help alleviate the bias.
We propose to explicitly address this problem by learning an unsupervised
asymmetric distance metric based on cross-view clustering. The asymmetric
distance metric allows specific feature transformations for each camera view to
tackle the specific feature distortions. We then design a novel unsupervised
loss function to embed the asymmetric metric into a deep neural network, and
therefore develop a novel unsupervised deep framework named the DEep
Clustering-based Asymmetric MEtric Learning (DECAMEL). In such a way, DECAMEL
jointly learns the feature representation and the unsupervised asymmetric
metric. DECAMEL learns a compact cross-view cluster structure of Re-ID data,
and thus help alleviate the view-specific bias and facilitate mining the
potential cross-view discriminative information for unsupervised Re-ID.
Extensive experiments on seven benchmark datasets whose sizes span several
orders show the effectiveness of our framework.
