  In recent years, a growing body of research has focused on the problem of
person re-identification (re-id). The re-id techniques attempt to match the
images of pedestrians from disjoint non-overlapping camera views. A major
challenge of re-id is the serious intra-class variations caused by changing
viewpoints. To overcome this challenge, we propose a deep neural network-based
framework which utilizes the view information in the feature extraction stage.
The proposed framework learns a view-specific network for each camera view with
a cross-view Euclidean constraint (CV-EC) and a cross-view center loss (CV-CL).
We utilize CV-EC to decrease the margin of the features between diverse views
and extend the center loss metric to a view-specific version to better adapt
the re-id problem. Moreover, we propose an iterative algorithm to optimize the
parameters of the view-specific networks from coarse to fine. The experiments
demonstrate that our approach significantly improves the performance of the
existing deep networks and outperforms the state-of-the-art methods on the
VIPeR, CUHK01, CUHK03, SYSU-mReId, and Market-1501 benchmarks.
