  Identifying humans with their walking sequences, known as gait recognition,
is a useful biometric understanding task as it can be observed from a long
distance and does not require cooperation from the subject. Two common
modalities used for representing the walking sequence of a person are
silhouettes and joint skeletons. Silhouette sequences, which record the
boundary of the walking person in each frame, may suffer from the variant
appearances from carried-on objects and clothes of the person. Framewise joint
detections are noisy and introduce some jitters that are not consistent with
sequential detections. In this paper, we combine the silhouettes and skeletons
and refine the framewise joint predictions for gait recognition. With temporal
information from the silhouette sequences, we show that the refined skeletons
can improve gait recognition performance without extra annotations. We compare
our methods on four public datasets, CASIA-B, OUMVLP, Gait3D and GREW, and show
state-of-the-art performance.
