  Human gait is one of important biometric characteristics for human
identification at a distance. In practice, occlusion usually occurs and
seriously affects accuracy of gait recognition. However, there is no available
database to support in-depth research of this problem, and state-of-arts gait
recognition methods have not paid enough attention to it, thus this paper
focuses on gait recognition under occlusion. We collect a new gait recognition
database called OG RGB+D database, which breaks through the limitation of other
gait databases and includes multimodal gait data of various occlusions
(self-occlusion, active occlusion, and passive occlusion) by our multiple
synchronous Azure Kinect DK sensors data acquisition system (multi-Kinect SDAS)
that can be also applied in security situations. Because Azure Kinect DK can
simultaneously collect multimodal data to support different types of gait
recognition algorithms, especially enables us to effectively obtain
camera-centric multi-person 3D poses, and multi-view is better to deal with
occlusion than single-view. In particular, the OG RGB+D database provides
accurate silhouettes and the optimized human 3D joints data (OJ) by fusing data
collected by multi-Kinects which are more accurate in human pose representation
under occlusion. We also use the OJ data to train an advanced 3D multi-person
pose estimation model to improve its accuracy of pose estimation under
occlusion for universality. Besides, as human pose is less sensitive to
occlusion than human appearance, we propose a novel gait recognition method
SkeletonGait based on human dual skeleton model using a framework of siamese
spatio-temporal graph convolutional networks (siamese ST-GCN). The evaluation
results demonstrate that SkeletonGait has competitive performance compared with
state-of-art gait recognition methods on OG RGB+D database and popular CAISA-B
database.
