On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on model capable of boosting
the robustness of various single compact 2D gait representations.
Gait recognition is negatively influenced by covariate
factors including clothing and time which alter the
natural gait appearance and motion. Contrary to traditional
gait recognition, our bolt-on module remedies this by a dedicated
covariate factor detection and removal procedure which
we quantitatively and qualitatively evaluate. The fundamental
concept of the bolt-on module is founded on exploiting
the pixel-wise composition of covariates factors. Results
demonstrate how our bolt-on module is a powerful component
leading to significant improvements across gait representations
and datasets yielding state of the art results.