  In this work, we present a Multi-Channel deep convolutional Pyramid Person
Matching Network (MC-PPMN) based on the combination of the semantic-components
and the color-texture distributions to address the problem of person
re-identification. In particular, we learn separate deep representations for
semantic-components and color-texture distributions from two person images and
then employ pyramid person matching network (PPMN) to obtain correspondence
representations. These correspondence representations are fused to perform the
re-identification task. Further, the proposed framework is 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 literature, especially on the rank-1 recognition rate.
