  While gait recognition has seen many advances in recent years, the occlusion
problem has largely been ignored. This problem is especially important for gait
recognition from uncontrolled outdoor sequences at range - since any small
obstruction can affect the recognition system. Most current methods assume the
availability of complete body information while extracting the gait features.
When parts of the body are occluded, these methods may hallucinate and output a
corrupted gait signature as they try to look for body parts which are not
present in the input at all. To address this, we exploit the learned occlusion
type while extracting identity features from videos. Thus, in this work, we
propose an occlusion aware gait recognition method which can be used to model
intrinsic occlusion awareness into potentially any state-of-the-art gait
recognition method. Our experiments on the challenging GREW and BRIAR datasets
show that networks enhanced with this occlusion awareness perform better at
recognition tasks than their counterparts trained on similar occlusions.
