# Multi-View Gait Recognition Based on a Siamese Vision Transformer
## 4 Experiment and Analysis
### Comparison with the Latest Technology  
#### 4.4.1 Ablation Experiment  
At present, in many gait recognition studies, walking speed, clothing, and other characteristics are tested separately. As such, it can be considered an idealized experiment where some of the complexity of the data are eliminated by manual classification. This allows the model to obtain good results in NM classification. Other classifications, however, have poor accuracy. We suggest that, in real-world application scenarios, human appearance is highly uncertain. Therefore, the data obtained for different states are trained and verified separately, and the results cannot be used as the possible results of practical applications.  
We experimented with a mixture of data for various characteristics to maximize the complexity of the gait data. In addition, our structural design focuses on improving the accuracy of gait recognition from multiple perspectives, rather than using cross-perspective experimental methods.  
For the \(11\) views in the CASIA B dataset, our model (SMViT_Base) was compared with SPAE [31], GaitGAN [19, 20], Multi_View GAN [21], Slack Allocation GAN [32], GAN based on U-Net [33], and PoseGait [34] in terms of the internal recognition rate of non-cross-view offset views. That is, without considering cross-verification, verification experiments only considered the multi-view perspective. Due to the limitations of the experimental environment and equipment, we could not effectively restore the experimental results of the multiple comparative models. Therefore, we directly used the experimental results presented in the papers. Other model data, shown in Table 2, were taken from the average value of the three-state gait recognition rates from the same view in the same dataset. It can be seen that, for all the views, the model presented here showed significant improvements when compared to the other gait recognition models. Additionally, the average upgrade index exceeded 20 percentage points. It is verified that, in the task of multi-view recognition with a non-crossing view, the model proposed in this paper is better than the selected comparison model.  
At the same time, the average values for the 11 views of normal walking (NM), walking with a backpack (BG), and walking while wearing a jacket (CL) were compared. For this comparison, the training sets and verification sets of each model were taken from the same view. The proportion of internal training sets and verification sets for each view is 7:3, and the cross-verification of the view is not considered. From Figure 8, it can be seen that the red model, which was proposed in this paper, significantly increased the average value of multi-view mixed recognition rates, which increased by about 20 percentage points.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Comparison of Model Accuracy for Each View When Not Crossing Views**} \\ \hline  & \(\mathbf{0}^{\circ}\) & \(\mathbf{18}^{\circ}\) & \(\mathbf{36}^{\circ}\) & \(\mathbf{54}^{\circ}\) & \(\mathbf{72}^{\circ}\) & \(\mathbf{90}^{\circ}\) & \(\mathbf{108}^{\circ}\) & \(\mathbf{126}^{\circ}\) & \(\mathbf{144}^{\circ}\) & \(\mathbf{162}^{\circ}\) & \(\mathbf{180}^{\circ}\) \\ \hline