# Multi-View Gait Recognition Based on a Siamese Vision Transformer
## 4 Experiment and Analysis
### Comparison with the Latest Technology
#### 4.4.2 Validation of the Gradually Moving-View Training Strategy  
As shown in Figure 7, we initially demonstrated the effectiveness of this strategy by comparing the T model with the base model in SMViT. During the experiment, we found that, with this training strategy, SMViT can still maximize the stability of learning for some perspectives with low-quality data. Taking the 18\({}^{\circ}\) gait data as an example, we conducted ablation experiments to verify the effectiveness of the gradually moving-view training method. As shown by the square point line in Figure 9, during the first 15 rounds of training, the SMViT_base model proposed in this paper dropped significantly in the second and ninth rounds and there was a significant saturation of recognition rates. After the experimental analysis, we surmised that the first decline was due to the loss of a number of gait characteristic outlines. At the same time, this model does not use view-mobilization training methods to convert and strengthen the gait characteristics, making the model unable to effectively learn the characteristics, and the accuracy decreases sharply. The second drop is due to the small number of features, which led to the abnormal situation of gradual overfitting in the verification accuracy; this also reflects the improvement in the model's robustness facilitated by the view-transition training method. At around 13 rounds, the base model reaches the upper limit of saturation accuracy but is still about one percentage point lower than the SMViT_T model's recognition accuracy. On the whole, due to the gradually moving-view training strategy of SMViT, the initial recognition rate is about 70 percentage points higher than that of the basic model; our model also maintains a relatively stable level of recognition accuracy. Although the accuracy saturation trend also appeared quickly, the upper limit of the saturation value was about one percentage point higher than that of the base model, and the oscillation amplitude of the validation rate remained below one percentage point.  
In this paper, we integrated the design concept of Siamese neural networks and a variant mobile vision transformer model and built a multi-view Siamese ViT gait recognition model: SMViT. At the same time, we designed a gradually moving-view training strategy for multi-view gait recognition, referred to as SMViT_Base and SMViT_T. After conducting a number of experiments on the CASIA B dataset, it was shown that the Siamese feature relationship calculation method can be used to obtain the perspective characteristic conversion factor, which can be used to determine the relationship between different perspective gait characteristics; this effectively improves the accuracy of multi-perspective gait recognition. Our experimental results show that the proposed model can significantly improve the recognition rate when compared with the existing generative multi-view gait recognition methods, without considering cross-view verification. We demonstrated an increase of 20 percentage points in the hybrid recognition rate, without considering the external attire of the pedestrians. Therefore, SMViT expands the gait recognition view while ensuring high accuracy, improving efficient gait recognition in multi-view practical application scenarios.  
Figure 8: Comparison of the average validation rates of the model from multiple views.