There are several confounding factors that can reduce the accuracy of gait
recognition systems. These factors can reduce the distinctiveness, or alter the
features used to characterise gait, they include variations in clothing,
lighting, pose and environment, such as the walking surface. Full invariance to
all confounding factors is challenging in the absence of high-quality labelled
training data. We introduce a simulation-based methodology and a
subject-specific dataset which can be used for generating synthetic video
frames and sequences for data augmentation. With this methodology, we generated
a multi-modal dataset. In addition, we supply simulation files that provide the
ability to simultaneously sample from several confounding variables. The basis
of the data is real motion capture data of subjects walking and running on a
treadmill at different speeds. Results from gait recognition experiments
suggest that information about the identity of subjects is retained within
synthetically generated examples. The dataset and methodology allow studies
into fully-invariant identity recognition spanning a far greater number of
observation conditions than would otherwise be possible.