  While the Vision Transformer has been used in gait recognition, its
application in multi-view gait recognition is still limited. Different views
significantly affect the extraction and identification accuracy of the
characteristics of gait contour. To address this, this paper proposes a Siamese
Mobile Vision Transformer (SMViT). This model not only focuses on the local
characteristics of the human gait space but also considers the characteristics
of long-distance attention associations, which can extract multi-dimensional
step status characteristics. In addition, it describes how different
perspectives affect gait characteristics and generate reliable perspective
feature relationship factors. The average recognition rate of SMViT on the
CASIA B data set reached 96.4%. The experimental results show that SMViT can
attain state-of-the-art performance compared to advanced step recognition
models such as GaitGAN, Multi_view GAN, Posegait and other gait recognition
models.
