# Lightweight Facial Expression Recognition Based on Class-Rebalancing Fusion Cumulative Learning
## 4 Experiment and Analysis
### Ablation Experiments  
To show the availability and rationality of our method, ablation experiments are performed for each module, and the experimental results are shown in Table 3. RM denotes the promoted random masking method, Clip_K5_ShuffleNet denotes the improved inverted residual structure, SA denotes the shuffle attention mechanism module, RLR-CNet denotes the dual-branch network with cumulative learning, and RM + Clip_K5_ShuffleNet + SA + RLR-CNet denotes the lightweight network proposed in our paper. Firstly, using ShuffleNet as the base network, the training samples are input into the network through a promoted random masking operation, and in order to prevent information loss and accelerate the speed of network operation, the lightweight ShuffleNet is improved to obtain a lighter Clip_K5_ShuffleNet network. To further extract local facial expression detail features, the SA module is integrated into the network to redistribute the feature weights of facial expressions from the two channel and spatial dimensions. To ensure the practicability and effectiveness of the network in real scenes, RLR-CNet is proposed in this paper. The effectiveness of each improvement module in the network is shown in Table 3.  
In Table 3, it can be seen that after RM processing, the recognition accuracy of ShuffleNet on FER2013, CK+, and RAF-DB datasets increases by 2.16%, 0.68%, and 0.80%, respectively. The introduction of Clip_K5_ShuffleNet leads to a slight decrease in the recognition accuracy of the network, but on the other hand, it reduces the number of parameters, which is beneficial for accelerating the operation speed of the model. The introduction of the SA module further improves the recognition accuracy of the network. Compared to the original network, our RLR-CNet promotes the recognition accuracy of the network by 5.43%, 2.81%, and 3.62%, and reduces the number of parameters by 17.74%. This shows that our method in the paper has some advantages in recognition accuracy and lightweight parameters. The experimental training process of the proposed method in this paper is shown in Figure 8. For Figure 8a, the recognition accuracy growth of the model slows down at the 100 th epoch, and the accuracy gradually becomes stable when it reaches the 200 th epoch, with the highest accuracy reaching 71.14%. For Figure 8b, the recognition accuracy of the model rapidly increases at the beginning of the training, and when it reaches the 150 th epoch, the accuracy tends to stabilize, with the highest accuracy reaching 98.04%. For Figure 8c, the recognition accuracy of the model continues to increase, and when it reaches the 180 th epoch, the accuracy gradually becomes stable, with the highest accuracy reaching 87.93%. For Figure 8, the accuracy of the training set is always less than or equal to the accuracy of testing sets, and the accuracy of the two sets is generally very close, indicating \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **FER2013** & **CK+** & **RAF-DB** & **Parameter** \\ \hline ShuffleNet & 65.71\% & 95.23\% & 84.31\% & 1.24 MB \\ ShuffleNet + RM & 67.87\% & 95.91\% & 85.11\% & 1.24 MB \\ ShuffleNet + RM + Clip\_K5 & 67.84\% & 95.87\% & 84.98\% & 0.94 MB \\ ShuffleNet + RM + Clip\_K5 + SA & 69.35\% & 96.35\% & 86.44\% & 0.96 MB \\ RLR-CNet (ours) & 71.14\% & 98.04\% & 87.93\% & 1.02 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different modules of network. that the method proposed in this paper can better capture the data characteristics and fit the data, and has better generalization. For exploring more accurately the recognition performance of our method for FER, the confusion matrices are drawn based on the experimental results on the FER2013, CK+, and RAF-DB datasets, as shown in Figure 9. For the FER2013 dataset, the recognition accuracy of the happy and surprised expressions is relatively high, both exceeding 80%. For the CK+ dataset, all facial expressions' recognition accuracies are above 95%.