# Update class center using momentum-based smoothing
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.3.3 Ablation Experiment  
Experiments on the datasets Fer2013, CK+ and RAF-DB with different improvement schemes can verify the effect of each part of the module on the model as a whole, and the experimental results are shown in Table 6. In Table 6, 1 stands for deep and shallow feature fusion function, 2 stands for channel and spatial attention mechanisms, 3 represents improved center loss function. Analyzing Table 6, it can be seen that in Scenario 1, the combination of the backbone network Base and the deep and shallow feature fusion module has 3.12 M Params and 286.73 M FLOPs, with an Accuracy of 65.26% on the Fer2013 dataset, 94.73% on the CK+ dataset, and 81.37% on the RAF-DB dataset, which shows that, in order to seamlessly fuse the deep and shallow networks, LightExNet has streamlined the architecture of MobileNet V2. Although the deep and shallow feature fusion module has been added, its Params and FLOPS parameters are still slightly lower than those of MobileNet V2, and the accuracy on the three datasets is basically equivalent to MobileNet V2, which proves the effectiveness of the deep and shallow feature fusion module in face expression recognition. In Scheme 2, the channel and spatial attention mechanism is added, and its Params and FLOPs are increased to 3.23 M and 294.93 M, respectively, and the Accuracy is 67.96% on the Fer2013 dataset, 96.12% on the CK+ dataset, and 84.34% on the RAF-DB dataset. Comparing with Scheme 1, the model complexity becomes slightly higher, but the recognition accuracy is improved, indicating that the channel and spatial attention mechanism can effectively improve the feature extraction ability of the model. In Scenario 3, compared with Scenario 1, only the improved central loss function is added, and its Params and FLOPs are slightly increased to 3.16 M and 289.76 M, respectively. The accuracy is 65.97% on the Fer2013 dataset, 95.70% on the CK+ dataset, and 83.11% on the RAF-DB dataset. Comparing with Scheme 1, it can be seen that the improved central loss function can effectively improve the classification performance and generalization ability of the model, and there is no significant increase in Params and FLOPs by the measure of mitigating the extra computational burden brought by the central loss function. In Scheme 4, both the channel and spatial attention mechanisms, and the improved central loss function are introduced, with Params of 3.27 M, FLOPs of 298.27 M, and the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scheme** & **Base** & 1 & 2 & 3 & **Params/M** & **FLOPs/M** & **Fer2013** & **CK+** & **RAF-DB** \\
1 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 3.12 & 286.73 & 65.26 & 94.73 & 81.37 \\
2 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 3.23 & 294.93 & 67.96 & 96.12 & 84.34 \\
3 & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 3.16 & 289.76 & 65.97 & 95.70 & 83.11 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 3.27 & 298.27 & 69.17 & 97.37 & 85.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment of LightExNet. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Fer2013 Acc/\%** & **CK+ Acc/\%** & **RAF-DB Acc/\%** \\ \hline Self-Cure Net [21] & 66.17 & \(\surd\) & \(\surd\) \\ Improved & 62.2 & \(\surd\) & \(\surd\) \\ MobileViT [33] & 67.68 & 95.91 & 85.39 \\ MFN+ [34] & \(\surd\) & \(\surd\) & 82.43 \\ Ada-CM [23] & \(\surd\) & \(\surd\) & 85.02 \\ Parallel CNN [35] & 65.60 & 94.03 & 83.01 \\
**Ours** & **69.17** & **97.37** & **85.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._accuracy on the Fer2013 dataset is 69.17%, on the CK+ dataset is 97.37%, and on the RAF-DB dataset is 85.97%, which is still slightly lower in terms of the number of parameters compared to MobileNet V2, but the Accuracy on the three datasets does improve in some ways, proving LightExNet is more suitable for the task of recognizing facial expressions.