  In this paper, we observe that most false positive images (i.e., different
identities with query images) in the top ranking list usually have the similar
color information with the query image in person re-identification (Re-ID).
Meanwhile, when we use the greyscale images generated from RGB images to
conduct the person Re-ID task, some hard query images can obtain better
performance compared with using RGB images. Therefore, RGB and greyscale images
seem to be complementary to each other for person Re-ID. In this paper, we aim
to utilize both RGB and greyscale images to improve the person Re-ID
performance. To this end, we propose a novel two-stream deep neural network
with RGB-grey information, which can effectively fuse RGB and greyscale feature
representations to enhance the generalization ability of Re-ID. Firstly, we
convert RGB images to greyscale images in each training batch. Based on these
RGB and greyscale images, we train the RGB and greyscale branches,
respectively. Secondly, to build up connections between RGB and greyscale
branches, we merge the RGB and greyscale branches into a new joint branch.
Finally, we concatenate the features of all three branches as the final feature
representation for Re-ID. Moreover, in the training process, we adopt the joint
learning scheme to simultaneously train each branch by the independent loss
function, which can enhance the generalization ability of each branch. Besides,
a global loss function is utilized to further fine-tune the final concatenated
feature. The extensive experiments on multiple benchmark datasets fully show
that the proposed method can outperform the state-of-the-art person Re-ID
methods. Furthermore, using greyscale images can indeed improve the person
Re-ID performance.
