  Visible-infrared cross-modality person re-identification (VI-ReId) is an
essential task for video surveillance in poorly illuminated or dark
environments. Despite many recent studies on person re-identification in the
visible domain (ReId), there are few studies dealing specifically with VI-ReId.
Besides challenges that are common for both ReId and VI-ReId such as
pose/illumination variations, background clutter and occlusion, VI-ReId has
additional challenges as color information is not available in infrared images.
As a result, the performance of VI-ReId systems is typically lower than that of
ReId systems. In this work, we propose a four-stream framework to improve
VI-ReId performance. We train a separate deep convolutional neural network in
each stream using different representations of input images. We expect that
different and complementary features can be learned from each stream. In our
framework, grayscale and infrared input images are used to train the ResNet in
the first stream. In the second stream, RGB and three-channel infrared images
(created by repeating the infrared channel) are used. In the remaining two
streams, we use local pattern maps as input images. These maps are generated
utilizing local Zernike moments transformation. Local pattern maps are obtained
from grayscale and infrared images in the third stream and from RGB and
three-channel infrared images in the last stream. We improve the performance of
the proposed framework by employing a re-ranking algorithm for post-processing.
Our results indicate that the proposed framework outperforms current
state-of-the-art with a large margin by improving Rank-1/mAP by 29.79%/30.91%
on SYSU-MM01 dataset, and by 9.73%/16.36% on RegDB dataset.
