  At present, the existing gait recognition systems are focusing on developing
methods to extract robust gait feature from silhouette images and they indeed
achieved great success. However, gait can be sensitive to appearance features
such as clothing and carried items. Compared with appearance-based method,
model-based gait recognition is promising due to the robustness against these
variations. In recent years, with the development of human pose estimation, the
difficulty of model-based gait recognition methods has been mitigated. In this
paper, to resist the increase of subjects and views variation, local features
are built and a siamese network is proposed to maximize the distance of samples
from the same subject. We leverage recent advances in action recognition to
embed human pose sequence to a vector and introduce Spatial-Temporal Graph
Convolution Blocks (STGCB) which has been commonly used in action recognition
for gait recognition. Experiments on the very large population dataset named
OUMVLP-Pose and the popular dataset, CASIA-B, show that our method archives
some state-of-the-art (SOTA) performances in model-based gait recognition. The
code and models of our method are available at
https://github.com/timelessnaive/Gait-for-Large-Dataset after being accepted.
