  Gait recognition is widely used in diversified practical applications.
Currently, the most prevalent approach is to recognize human gait from RGB
images, owing to the progress of computer vision technologies. Nevertheless,
the perception capability of RGB cameras deteriorates in rough circumstances,
and visual surveillance may cause privacy invasion. Due to the robustness and
non-invasive feature of millimeter wave (mmWave) radar, radar-based gait
recognition has attracted increasing attention in recent years. In this
research, we propose a Hierarchical Dynamic Network (HDNet) for gait
recognition using mmWave radar. In order to explore more dynamic information,
we propose point flow as a novel point clouds descriptor. We also devise a
dynamic frame sampling module to promote the efficiency of computation without
deteriorating performance noticeably. To prove the superiority of our methods,
we perform extensive experiments on two public mmWave radar-based gait
recognition datasets, and the results demonstrate that our model is superior to
existing state-of-the-art methods.
