  Gait, the walking pattern of individuals, is one of the important biometrics
modalities. Most of the existing gait recognition methods take silhouettes or
articulated body models as gait features. These methods suffer from degraded
recognition performance when handling confounding variables, such as clothing,
carrying and viewing angle. To remedy this issue, we propose a novel
AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical
and pose features from RGB imagery. The LSTM integrates pose features over time
as a dynamic gait feature while canonical features are averaged as a static
gait feature. Both of them are utilized as classification features. In
addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait
recognition from frontal-view walking, which is a challenging problem since it
contains minimal gait cues compared to other views. FVG also includes other
important variations, e.g., walking speed, carrying, and clothing. With
extensive experiments on CASIA-B, USF, and FVG datasets, our method
demonstrates superior performance to the SOTA quantitatively, the ability of
feature disentanglement qualitatively, and promising computational efficiency.
We further compare our GaitNet with state-of-the-art face recognition to
demonstrate the advantages of gait biometrics identification under certain
scenarios, e.g., long distance/lower resolutions, cross viewing angles.
