  Gait recognition is a biometric technology that recognizes the identity of
humans through their walking patterns. Compared with other biometric
technologies, gait recognition is more difficult to disguise and can be applied
to the condition of long-distance without the cooperation of subjects. Thus, it
has unique potential and wide application for crime prevention and social
security. At present, most gait recognition methods directly extract features
from the video frames to establish representations. However, these
architectures learn representations from different features equally but do not
pay enough attention to dynamic features, which refers to a representation of
dynamic parts of silhouettes over time (e.g. legs). Since dynamic parts of the
human body are more informative than other parts (e.g. bags) during walking, in
this paper, we propose a novel and high-performance framework named DyGait.
This is the first framework on gait recognition that is designed to focus on
the extraction of dynamic features. Specifically, to take full advantage of the
dynamic information, we propose a Dynamic Augmentation Module (DAM), which can
automatically establish spatial-temporal feature representations of the dynamic
parts of the human body. The experimental results show that our DyGait network
outperforms other state-of-the-art gait recognition methods. It achieves an
average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D
dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-MVLP dataset.
