  Matching pedestrians across multiple camera views, known as human
re-identification, is a challenging research problem that has numerous
applications in visual surveillance. With the resurgence of Convolutional
Neural Networks (CNNs), several end-to-end deep Siamese CNN architectures have
been proposed for human re-identification with the objective of projecting the
images of similar pairs (i.e. same identity) to be closer to each other and
those of dissimilar pairs to be distant from each other. However, current
networks extract fixed representations for each image regardless of other
images which are paired with it and the comparison with other images is done
only at the final level. In this setting, the network is at risk of failing to
extract finer local patterns that may be essential to distinguish positive
pairs from hard negative pairs. In this paper, we propose a gating function to
selectively emphasize such fine common local patterns by comparing the
mid-level features across pairs of images. This produces flexible
representations for the same image according to the images they are paired
with. We conduct experiments on the CUHK03, Market-1501 and VIPeR datasets and
demonstrate improved performance compared to a baseline Siamese CNN
architecture.
