# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 4 Experiments
### Ablation Study  
In this paper, the three components (MSFE, FLSFE and MSTFE) of the two-path spatial-temporal feature fusion module and the view embedding module are included in the proposed framework. Hereby, exhaustive ablation experiments of these components are carried out on CASIA-B dataset to assess the effectiveness of each individual component. The experimental results and analysis are presented below. **Impact of Each component:** Table 3 illustrates the effect of each component in our method on CASIA-B dataset. The baseline refers to the remaining structure after removing MSFE, FLSFE, MSTFE and view embedding module from the proposed methods, which  
consists of three-layer CNNs and aggregates temporal information through the max operation. It can be observed from Table 3 that when MSFE is added to baseline alone, the average accuracy under three walking conditions achieves +1.5% improvement than the baseline, which implies that MSFE can perform well on the baseline by using different kernel sizes to handle the input. In addition, with the utilization of FLSFE and MSTFE, the average accuracy reaches 90.5%, which is +1.7% higher than GaitPart [7] and the accuracies under each condition are improved significantly, especially under BG and CL, proving the superiority of the two-path spatial-temporal feature fusion module under occlusion. Particularly, when FLSFE is added to the baseline, the average accuracy increases substantially by +7.9% compared to Baseline + MSFE, which verifies the fact that FLSFE plays a vitally important role in this two-path parallel structure. Finally, we incorporate view embedding into the baseline to increase the recognition accuracy of our method, resulting in a remarkable 91.7% performance, which is +1.2% higher than the previous version. The comparison results confirm that view prediction offers valuable perspective information for cross-view gait recognition and the view embedding module proves to be instrumental in improving the recognition capability of multi-view gait tasks. **Impact of different kernel sizes in MSFE:** To study the impact of various kernel sizes in MSFE, three various kinds of kernel sizes are designed and the ablation studies are conducted by arranging them. The results of these experiments are presented in Table 4. Evidently, increasing the number of multi-scale convolution kernels can attain higher recognition accuracies. Therefore, we set the kernel sizes of the multi-scale convolution to 1, 3, and 5, as it yields the most favorable recognition performance. **Impact of the value of \(n\) in R-conv:** Following the approach of setting the parameter \(n\) in R-conv as described in Section 3.2.2, five controlled experiments are performed and the results are presented in Table 5. Notably, when the \(n\) value of all the R-conv is set to 1, the R-conv becomes entirely composed of regular layers. From Table 5. it becomes evident that when the \(n\) value of three consecutive R-conv is set to 2, 8, 8, the recognition accuracy achieves the most excellent performance compared to other four experiments. There is another thing worth noting that it is not the case that the larger \(n\) is, the higher recognition accuracy will be. For example, when the \(n\) value of three consecutive R-conv is set to 4, 8, 8, the accuracy under NM increases slightly, while the accuracies under BG and CL decrease dramatically. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel Size** & **NM** & **BG** & **CL** \\ \hline
1 & 89.8 & 82 & 59.5 \\
1, 3 & 91.3 & 82.8 & 60.2 \\
1, 5 & 90.9 & 82.6 & 59.9 \\
1, 3, 5 & **91.6** & **83.5** & **60.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Study of the effectiveness of different kernel sizes in MSFE on CASIA-B dataset.