  Radar gait recognition is robust to light variations and less infringement on
privacy. Previous studies often utilize either spectrograms or cadence velocity
diagrams. While the former shows the time-frequency patterns, the latter
encodes the repetitive frequency patterns. In this work, a dual-stream neural
network with attention-based fusion is proposed to fully aggregate the
discriminant information from these two representations. The both streams are
designed based on the Vision Transformer, which well captures the gait
characteristics embedded in these representations. The proposed method is
validated on a large benchmark dataset for radar gait recognition, which shows
that it significantly outperforms state-of-the-art solutions.
