Multi-View Gait Recognition Based on a Siamese Vision Transformer

Although the vision transformer has been used in gait recognition, its application in multi-view gait recognition remains limited. Different views significantly affect the accuracy with which the characteristics of gait contour are extracted and identified. To address this issue, 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 the gait characteristics and generates reliable features of perspective-relationship factors. The average recognition rate of SMViT for the CASIA B dataset reached 96.4%. The experimental results show that SMViT can attain a state-of-the-art performance when compared to advanced step-recognition models, such as GaitGAN, Multi_view GAN and Posegait.

Automated summary

This paper proposes a Siamese mobile vision transformer (SMViT) model for multi-view gait recognition. The model focuses on both local characteristics and long-distance attention associations, and can extract multi-dimensional step status features as well as describe the influence of different perspectives on gait characteristics.