# Exploring Gait Recognition in Wild Nighttime Scenes
## 5 Results and Discussion
### Ablation Study  
#### 5.2.1 Analysis of Input Feature Types  
We compared the performance differences between models trained using joint point data or skeleton data alone, and models trained using both features simultaneously. The experimental results are shown in Table 3. Using either joint point or skeleton features independently failed to achieve optimal results. However, when combining both joint point and skeleton features, GaitSAT achieved the best performance across multiple datasets. Notably, on GaitDN, compared to using joint point and skeleton features separately, the combined use of both features increased the recognition accuracy by 14.11% and 5.29%, respectively. This result indicates that integrating joint point and skeleton features captures richer gait information, thereby enhancing the model recognition capability. The superior performance of integrating joint and skeleton features emerges from their intrinsically complementary characteristics. Specifically, joint features excel at capturing local motion patterns and precise positioning information, while skeleton features provide comprehensive global structural insights and body configuration. When strategically integrated, these features synergistically construct a more robust and nuanced representation, demonstrating exceptional adaptability, particularly in challenging environmental conditions such as low-light or nighttime scenarios, where individual feature types could independently exhibit limitations. Furthermore, we observed that compared to the CASIA-B indoor dataset, GaitSAT achieved more significant performance improvements when using both features in the outdoor datasets Gait3D and GaitDN. This finding emphasizes the importance and necessity of considering multiple features when addressing complex gait recognition tasks in outdoor scenarios. This not only validates the potential of our method in practical applications but also provides important insights for future gait recognition research in more complex and variable environments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Batchsize**} & \multirow{2}{*}{**Iterations**} & \multicolumn{2}{c}{**Spatial Feature Extractor**} & \multicolumn{2}{c}{**Multi-Scale Temporal Rhythm Perception Module**} \\ \cline{4-5}  & & & **Output Channels** & **Number of SAT Units** & **Output Channels** \\ \hline GaitDN & (32, 4) & 30 k & (64, 64, 128) & 2 & (128, 256) \\ \hline CASIA-B & (4, 32) & 40 k & (64, 64, 128) & 1 & (256) \\ \hline OUMVLP & (32, 16) & 150 k & (64, 128, 128) & 3 & (128, 256, 256) \\ \hline GREW & (32, 8) & 200 k & (64, 128, 128) & 3 & (128, 256, 256) \\ \hline Gait3D & (32, 4) & 60 k & (64, 64, 128) & 2 & (128, 256) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training parameters and network parameters for different datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Setting**} & \multirow{2}{*}{**CASIA-B**} & \multirow{2}{*}{**Gait3D**} & \multirow{2}{*}{**GaitDN**} \\ \hline
**Joints** & & & & \\ \hline \(\checkmark\) & & & 73.56 & 19.30 & 72.13 \\ \hline  & & \(\checkmark\) & 72.44 & 17.80 & 80.95 \\ \hline \(\checkmark\) & & \(\checkmark\) & **76.32** & **22.90** & **86.24** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of input feature types, in which we keep the network architecture consistent.