GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition.

In image recognition tasks, subjects with long distances and low resolution remain a challenge, whereas gait recognition, identifying subjects by walking patterns, is considered one of the most promising biometric technologies due to its stability and efficiency. Previous gait recognition methods mostly focused on constructing a sophisticated model structure for better model performance during evaluation. Moreover, these methods are primarily based on traditional convolutional neural networks (CNNs) due to the dominance of CNNs in computer vision. However, since the alternative form of Transformer, named Vision Transformers (ViTs), has been introduced into the computer vision field, the ViTs have gained strong attention for its outstanding performance in various tasks. Thus, unlike previous methods, this project introduces two Transformer-based methods: a completely ViTs-based method GaitTriViT, and a Video Vision Transformer (Video ViT) based method GaitVViT. The GaitTriViT leverages the ViTs to gain more fine-grained spatial features, while GaitVViT enhances the capacity of temporal extraction. This work evaluates their performances and the results show the still-existing gaps and several encouraging outperforms compared with current state-of-the-art (SOTA), demonstrating the difficulties and challenges these Transformer-based methods will encounter continuously. However, the future of Vision Transformers in gait recognition is still promising.