# Lightweight Facial Expression Recognition Based on Class-Rebalancing Fusion Cumulative Learning
## 4 Experiment and Analysis
### Comparative Experiment of Mainstream Algorithms  
To prove the effectiveness of the RLR-CNet proposed in this paper for FER, a comparative experiment is carried out with several mainstream algorithms such as ResNet18, ResNet50, VGG16, VGG19, and AlexNet from the aspects of the number of parameters of the model and the recognition accuracy. The experimental results are shown in Table 4.  
For the FER2013 dataset of the facial expressions' recognition accuracy, the proposed method in our paper achieves the highest accuracy among all the models, with an improvement of approximately 1.05% compared to ResNet18. For the CK+ dataset, the recognition accuracy of VGG16 is higher than those of other mainstream networks, while the proposed \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FER2013 (\%)** & **CK+ (\%)** & **RAF-DB (\%)** & **Parameter (MB)** \\ \hline ResNet18 & 70.09 & 89.39 & 84.10 & 11.69 \\ ResNet50 & 71.26 & 92.46 & 86.01 & 25.56 \\ VGG16 & 68.89 & 95.46 & 81.68 & 14.75 \\ VGG19 & 68.53 & 92.18 & 81.17 & 20.06 \\ AlexNet & 67.51 & 87.59 & 55.60 & 60.92 \\ ours & 71.14 & 98.04 & 87.93 & 1.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison experiments of mainstream algorithms. Figure 9: Confusion matrix. (**a**) Confusion matrix on the FER2013 dataset; (**b**) confusion matrix on the CK+ dataset; (**c**) confusion matrix on the RAF-DB dataset. method in our paper achieves an improvement of 2.58% compared to VGG16. For the RAF-DB dataset, the recognition accuracy of the proposed method in our paper reaches 87.93%. The improvement in the recognition accuracy further verifies the effectiveness and strong generalization ability of the proposed method. In terms of model parameters, the parameter size of the proposed model is 1.02 MB, which is lowest among the mainstream algorithms. In general, while achieving a lightweight model, our method ensures great recognition accuracy, and then it demonstrates the efficiency and superiority of this paper's method. To further prove the effectiveness of our method, we conduct comparative experiments with some existing advanced methods on FER2013, CK+, and RAF-DB datasets. The advanced methods mainly include DCN and Inception V4, which are very novel in recent years, as well as lightweight networks such as Mini-Xception, MFN, and MANet, and classification networks embedded with modules such as SE and CBAM attention mechanisms. The experimental results are shown in Tables 5-7, respectively. It can be observed from the experimental results that, for the FER2013 dataset, the recognition accuracy is above 65%, while our method achieves an accuracy of 71.14%. For the CK+ dataset, the recognition accuracy  
facial expressions, and then improves the accuracy of FER on the lightweight network as a whole. According to the experimental results, the accuracy of the proposed method is 71.14% on the FER2013 dataset, 98.04% on the CK+ dataset, and 87.93% on the RAF-DB dataset. The model contains fewer parameters and achieves great recognition accuracy while implementing a lightweight network, and its accuracy is better than most current mainstream algorithms, demonstrating better effectiveness and applicability. In this paper, the object of study is the single-label expression dataset. The single-label expression can well represent the emotions contained in various expressions and has a good recognition accuracy for common expression categories, but in real scenes, there are still some minor expression categories that are not taken into account, such as tension and pride. The recognition of these minor expressions needs to be achieved by studying composite multi-label expressions. Therefore, in future research, more attention should be paid to the study of compound multi-label facial expression recognition. Conceptualization, X.M. and Y.S. ; methodology, X.M., Y.S. and R.W. ; experimental test, Y.S. ; writing--original draft preparation, Y.S. and X.M. ; writing--review and editing, X.M., Y.S., R.W., Y.T. and Y.X. ; supervision, X.M., R.W. and Y.T.