  Gait recognition is a promising video-based biometric for identifying
individual walking patterns from a long distance. At present, most gait
recognition methods use silhouette images to represent a person in each frame.
However, silhouette images can lose fine-grained spatial information, and most
papers do not regard how to obtain these silhouettes in complex scenes.
Furthermore, silhouette images contain not only gait features but also other
visual clues that can be recognized. Hence these approaches can not be
considered as strict gait recognition.
  We leverage recent advances in human pose estimation to estimate robust
skeleton poses directly from RGB images to bring back model-based gait
recognition with a cleaner representation of gait. Thus, we propose GaitGraph
that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a
modern model-based approach for gait recognition. The main advantages are a
cleaner, more elegant extraction of the gait features and the ability to
incorporate powerful spatio-temporal modeling using GCN. Experiments on the
popular CASIA-B gait dataset show that our method archives state-of-the-art
performance in model-based gait recognition.
  The code and models are publicly available.
