  Visible-Infrared person re-identification (VI-ReID) is a challenging matching
problem due to large modality varitions between visible and infrared images.
Existing approaches usually bridge the modality gap with only feature-level
constraints, ignoring pixel-level variations. Some methods employ GAN to
generate style-consistent images, but it destroys the structure information and
incurs a considerable level of noise. In this paper, we explicitly consider
these challenges and formulate a novel spectrum-aware feature augementation
network named SFANet for cross-modality matching problem. Specifically, we put
forward to employ grayscale-spectrum images to fully replace RGB images for
feature learning. Learning with the grayscale-spectrum images, our model can
apparently reduce modality discrepancy and detect inner structure relations
across the different modalities, making it robust to color variations. In
feature-level, we improve the conventional two-stream network through balancing
the number of specific and sharable convolutional blocks, which preserve the
spatial structure information of features. Additionally, a bi-directional
tri-constrained top-push ranking loss (BTTR) is embedded in the proposed
network to improve the discriminability, which efficiently further boosts the
matching accuracy. Meanwhile, we further introduce an effective dual-linear
with batch normalization ID embedding method to model the identity-specific
information and assits BTTR loss in magnitude stabilizing. On SYSU-MM01 and
RegDB datasets, we conducted extensively experiments to demonstrate that our
proposed framework contributes indispensably and achieves a very competitive
VI-ReID performance.
