  Most recent person re-identification approaches are based on the use of deep
convolutional neural networks (CNNs). These networks, although effective in
multiple tasks such as classification or object detection, tend to focus on the
most discriminative part of an object rather than retrieving all its relevant
features. This behavior penalizes the performance of a CNN for the
re-identification task, since it should identify diverse and fine grained
features. It is then essential to make the network learn a wide variety of
finer characteristics in order to make the re-identification process of people
effective and robust to finer changes. In this article, we introduce Deep
Miner, a method that allows CNNs to "mine" richer and more diverse features
about people for their re-identification. Deep Miner is specifically composed
of three types of branches: a Global branch (G-branch), a Local branch
(L-branch) and an Input-Erased branch (IE-branch). G-branch corresponds to the
initial backbone which predicts global characteristics, while L-branch
retrieves part level resolution features. The IE-branch for its part, receives
partially suppressed feature maps as input thereby allowing the network to
"mine" new features (those ignored by G-branch) as output. For this special
purpose, a dedicated suppression procedure for identifying and removing
features within a given CNN is introduced. This suppression procedure has the
major benefit of being simple, while it produces a model that significantly
outperforms state-of-the-art (SOTA) re-identification methods. Specifically, we
conduct experiments on four standard person re-identification benchmarks and
witness an absolute performance gain up to 6.5% mAP compared to SOTA.
