  As an emerging biological identification technology, vision-based gait
identification is an important research content in biometrics. Most existing
gait identification methods extract features from gait videos and identify a
probe sample by a query in the gallery. However, video data contains redundant
information and can be easily influenced by bagging (BG) and clothing (CL).
Since human body skeletons convey essential information about human gaits, a
skeleton-based gait identification network is proposed in our project. First,
extract skeleton sequences from the video and map them into a gait graph. Then
a feature extraction network based on Spatio-Temporal Graph Convolutional
Network (ST-GCN) is constructed to learn gait representations. Finally, the
probe sample is identified by matching with the most similar piece in the
gallery. We tested our method on the CASIA-B dataset. The result shows that our
approach is highly adaptive and gets the advanced result in BG, CL conditions,
and average.
