  Gait, the walking pattern of individuals, is one of the most important
biometrics modalities. Most of the existing gait recognition methods take
silhouettes or articulated body models as the gait features. These methods
suffer from degraded recognition performance when handling confounding
variables, such as clothing, carrying and view angle. To remedy this issue, we
propose a novel AutoEncoder framework to explicitly disentangle pose and
appearance features from RGB imagery and the LSTM-based integration of pose
features over time produces the gait feature. 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 state
of the arts quantitatively, the ability of feature disentanglement
qualitatively, and promising computational efficiency.
