## 4 Experiments
### Comparison with Existing Methods  
To evaluate the performance of the proposed SFCNet, we compare it with existing methods on two large benchmark datasets as shown in Tables 5 and 6. We divide existing 3D action recognition methods into skeleton-based and depth-based methods. In skeleton-based methods, we compare different backbone-based methods, including CNN [5], LSTM [15,46], and GCN [6,23,47]. For depth-based methods, we compare 2D image-based methods [19,35,48], 3D CNN-based methods [28], and 3D voxel-based methods [10]. The comparison results are reported in Tables 5 and 6. For the NTU RGB+D 60 dataset, the proposed SFCNet achieves 89.9% and 96.7% accuracy in the case of cross-subject and cross-view settings. In addition, we compare SFCNet with two methods based on multimodal data. Compared with ED-MHI [31], which combined depth and skeleton data, our method improves the accuracy by 4.3% in the cross-subject setting. TS-CNN-LSTM [49] fused data from three modalities, namely RGB, depth, and skeleton, but it is 2.6% and 4.9% lower than SFCNet in the cross-subject setting and cross-view settings, respectively. For the NTU RGB+D 120 dataset, SFCNet also achieves competitive results, achieving accuracies of 83.6% and 93.8% under cross-subject and cross-view settings, respectively. In general, SFCNet is effective and excellent, which outperforms traditional methods using manual feature extraction [19,35,50] and deep learning methods that compress depth video into images for processing [2,3,36] or point cloud sequences [10]. The experimental results prove that the SFCNet is superior for capturing discriminative human behavior patterns and thus is beneficial to 3D action recognition. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Stream** & **Cross-Subject** & **Cross-View** \\ \hline
1s-SFCNet (L) & 85.0\% & 94.6\% \\
1s-SFCNet (G) & 81.8\% & 86.6\% \\ SFCNet (concat) & 88.9\% & 94.8\% \\ SFCNet (add) & 86.7\% & 93.9\% \\ SFCNet (fusion) & **89.9**\% & **96.7**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effectiveness of two-stream feature fusion on the NTU RGB+D 60 dataset. Figure 5: Feature attention visualization. We visualize the heat map of drinking water and punching. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Cross-Subject** & **Cross-Set** & **Year** \\ \hline \hline \multicolumn{4}{c}{**Input: 3D Skeleton**} \\ GCA-LSTM [15] & 58.3 & 59.3 & 2017 \\ Body pose evolution map [56] & 64.6 & 66.9 & 2018 \\ Two-stream attention LSTM [46] & 61.2 & 63.3 & 2018 \\ ST-GCN [23] & 70.7 & 73.2 & 2018 \\ NTU RGB+D 120 baseline [45] & 55.7 & 57.9 & 2019 \\ FSNet [57] & 59.9 & 62.4 & 2019 \\ SkeleMotion [5] & 67.7 & 66.9 & 2019 \\ TSH [20] & 67.9 & 62.8 & 2019 \\ AS-GCN [6] & 77.9 & 78.5 & 2019 \\
2s-AGCN [47] & 82.9 & 84.9 & 2019 \\ ST-TR (new) [24] & 82.7 & 84.7 & 2021 \\ SGMSN (new) [52] & 84.8 & 85.9 & 2023 \\ \multicolumn{4}{c}{**Input: Depth maps**} \\ APSR [45] & 48.7 & 40.1 & 2019 \\
3 DV-PointNet++ [10] & 82.4 & 93.5 & 2020 \\ DOGV (new) [53] & 82.2 & 85.0 & 2021 \\
3D-Pruning [54] & 76.6 & 88.8 & 2022 \\ SFCNet (ours) & **83.6** & **93.8** & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different methods for action recognition accuracy (%) on the NTU RGB+D 120 dataset.