  Visible-infrared person re-identification (V-I ReID) seeks to match images of
individuals captured over a distributed network of RGB and IR cameras. The task
is challenging due to the significant differences between V and I modalities,
especially under real-world conditions, where images are corrupted by, e.g,
blur, noise, and weather. Indeed, state-of-art V-I ReID models cannot leverage
corrupted modality information to sustain a high level of accuracy. In this
paper, we propose an efficient model for multimodal V-I ReID -- named
Multimodal Middle Stream Fusion (MMSF) -- that preserves modality-specific
knowledge for improved robustness to corrupted multimodal images. In addition,
three state-of-art attention-based multimodal fusion models are adapted to
address corrupted multimodal data in V-I ReID, allowing to dynamically balance
each modality importance. Recently, evaluation protocols have been proposed to
assess the robustness of ReID models under challenging real-world scenarios.
However, these protocols are limited to unimodal V settings. For realistic
evaluation of multimodal (and cross-modal) V-I person ReID models, we propose
new challenging corrupted datasets for scenarios where V and I cameras are
co-located (CL) and not co-located (NCL). Finally, the benefits of our Masking
and Local Multimodal Data Augmentation (ML-MDA) strategy are explored to
improve the robustness of ReID models to multimodal corruption. Our experiments
on clean and corrupted versions of the SYSU-MM01, RegDB, and ThermalWORLD
datasets indicate the multimodal V-I ReID models that are more likely to
perform well in real-world operational conditions. In particular, our ML-MDA is
an important strategy for a V-I person ReID system to sustain high accuracy and
robustness when processing corrupted multimodal images. Also, our multimodal
ReID model MMSF outperforms every method under CL and NCL camera scenarios.
