## 4 Design and Results of the Experiments
### Results of the Experiments
#### 4.3.2 Experiment 2  
The models are trained on RAF-DB, CK+, KDEF, and FER2013 before and after optimization and are verified by sampling in a certain proportion on original RAF-DB, CK+, JAFFE, KDEF, and FER2013. Among them, four datasets other than the training set are taken as the corresponding actual environment samples. In order to ensure the effectiveness of the experiment, the single validation sample set of the model obtained before and after the optimization of each specific training set is the same. In order to reduce contingency, five random samples are selected for each validation set, and the average recognition accuracy is taken as the result. The experimental results of the generalization performance are shown in Table 3.  
It can be perceived that the accuracy of the model optimized by the training set decreases slightly on the original training set. This is because the optimized model is trained based on the noise-adjusted training set, and the test sets are all original datasets. Therefore, the accuracy of the optimized model will inevitably decline in the original training set but will rise in other test sets that are not for training, which indicates that the  
Figure 6: Dynamic noise detection effect of different noise proportions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Training Dataset** & **Noise Adjustment 1** & **RAF-DB** & **CK+** & **JAFFE** & **KDEF** & **FER2013** \\ \hline RAF-DB & \(\times\) & **81.40\%** & 71.00\% & 39.50\% & 53.20\% & 51.10\% \\  & \(\checkmark\) & 80.80\% & **72.60\%** & **47.30\%** & **58.70\%** & **51.60\%** \\  & \(\times\) & 30.70\% & **99.40\%** & 25.60\% & **40.00\%** & 29.90\% \\ CK+ & \(\checkmark\) & **35.70\%** & 94.80\% & **30.20\%** & 36.10\% & **30.20\%** \\ KDEF & \(\times\) & 19.90\% & 29.70\% & 20.90\% & **77.50\%** & 21.40\% \\  & \(\checkmark\) & **26.50\%** & **48.70\%** & **30.20\%** & 76.90\% & **25.70\%** \\ FER2013 & \(\times\) & 60.80\% & 64.80\% & 41.90\% & 50.50\% & **71.30\%** \\  & \(\checkmark\) & **61.40\%** & **68.70\%** & **46.50\%** & **51.30\%** & 71.00\% \\ \hline \hline \end{tabular}  
* In the column ‘Noise Adjustment’, to the training set, \(\times\) means the noise adjustment hasn’t been made and \(\checkmark\) means the noise adjustment has been made.  
\end{table}
Table 3: Model recognition performance before and after noise adjustment in the training set.  
dependence on specific training sets has decreased. From Table 3, the verification accuracy on most other datasets has indeed improved. We can analyze the performance of each training set in its corresponding real sample environment before and after noise adjustment from Table 3. The assessment is shown in Table 4.  
It can be deduced from Table 4 that the generalization performance of the adjusted model has been improved to a certain extent. The Accuracy in other datasets is inferior for the training set KDEF. The reason is that KDEF has 4900 samples in total but includes only 70 subjects. The samples are photographed in the laboratory environment, and the image size, illumination, clarity, and posture are strictly controlled. The same expression of the same subject has multiple samples from different angles, but there is no difference in their facial expression characteristics. Therefore, its effective sample size is too small for deep neural network learning, resulting in overfitting. From the data, the relative increase for the model trained by KDEF before and after noise adjustment is observed to be much higher than the other training sets. It can be inferred that the more serious the overfitting, the better the optimization effect.  
The virtue of the purpose of this study is to build a lightweight expression recognition system and adopt confidence learning noise detection to optimize the practical application effect. Only the network structure constructed in Section 3.2 is tested, and other networks will not be further discussed in this article.