  In this work, we present a deep convolutional pyramid person matching network
(PPMN) with specially designed Pyramid Matching Module to address the problem
of person re-identification. The architecture takes a pair of RGB images as
input, and outputs a similiarity value indicating whether the two input images
represent the same person or not. Based on deep convolutional neural networks,
our approach first learns the discriminative semantic representation with the
semantic-component-aware features for persons and then employs the Pyramid
Matching Module to match the common semantic-components of persons, which is
robust to the variation of spatial scales and misalignment of locations posed
by viewpoint changes. The above two processes are jointly optimized via a
unified end-to-end deep learning scheme. Extensive experiments on several
benchmark datasets demonstrate the effectiveness of our approach against the
state-of-the-art approaches, especially on the rank-1 recognition rate.
