  The re-identification (ReID) of individuals over a complex network of cameras
is a challenging task, especially under real-world surveillance conditions.
Several deep learning models have been proposed for visible-infrared (V-I)
person ReID to recognize individuals from images captured using RGB and IR
cameras. However, performance may decline considerably if RGB and IR images
captured at test time are corrupted (e.g., noise, blur, and weather
conditions). Although various data augmentation (DA) methods have been explored
to improve the generalization capacity, these are not adapted for V-I person
ReID. In this paper, a specialized DA strategy is proposed to address this
multimodal setting. Given both the V and I modalities, this strategy allows to
diminish the impact of corruption on the accuracy of deep person ReID models.
Corruption may be modality-specific, and an additional modality often provides
complementary information. Our multimodal DA strategy is designed specifically
to encourage modality collaboration and reinforce generalization capability.
For instance, punctual masking of modalities forces the model to select the
informative modality. Local DA is also explored for advanced selection of
features within and among modalities. The impact of training baseline fusion
models for V-I person ReID using the proposed multimodal DA strategy is
assessed on corrupted versions of the SYSU-MM01, RegDB, and ThermalWORLD
datasets in terms of complexity and efficiency. Results indicate that using our
strategy provides V-I ReID models the ability to exploit both shared and
individual modality knowledge so they can outperform models trained with no or
unimodal DA. GitHub code: https://github.com/art2611/ML-MDA.
