# Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model
## 4 Results and Discussion
The results designate the RFER-EADL approach recognized all facial expressions accurately. For example, for class A, the RFER-EADL algorithm provided \(accu_{y}\) of 100%, \(sens_{y}\) of 100%, \(spec_{y}\) of 100%, and an MCC of 100%. Additionally, for class Co, the RFER-EADL approach granted \(accu_{y}\) of 99.40%, \(sens_{y}\) of 100%, \(spec_{y}\) of 99.39%, an \(F_{score}\) of 85.71%, and an MCC of 86.34%. Additionally, for class Di, the RFER-EADL approach provided \(accu_{y}\) of 98.81%, \(sens_{y}\) of 100%, \(spec_{y}\) of 98.70%, an \(F_{score}\) of 93.33%, and an MCC of 92.93%. Figure 10 shows the training accuracy (TRA) and validation accuracy (VLA) obtained by the RFER-EADL approach on the test dataset. The experimental results show that the RFER-EADL approach obtained higher TRA and VLA values. VLA appears to be greater than TRA. Figure 10: TRA and VLA analysis of the RFER-EADL algorithm. Figure 9: Average analysis of the RFER-EADL algorithm for 20% of the TS dataset. Figure 11 depicts the training loss (TRL) and validation loss (VLL) obtained by the RFER-EADL approach on the test dataset. The RFER-EADL approach produced experimental results with minimal TRL and VLL values. The VLL, in particular, is less than the TRL. Figure 12 depicts a clear precision-recall assessment of the RFER-EADL algorithm using the test dataset. The RFER-EADL technique, as depicted in the figure, resulted in high precision-recall values in each class label. Figure 13 depicts a quick ROC analysis of the RFER-EADL algorithm on the test dataset. The results demonstrate that the RFER-EADL approach is capable of classifying various classes. Figure 11: TRL and VLL analysis of the RFER-EADL algorithm. Figure 12: Precision–recall analysis of the RFER-EADL algorithm. A comparison of the RFER-EADL model with other DL models is shown in Table 5 and Figure 14[25]. These outcomes show that the LLDHF-FER and DSA-FER techniques reach lower \(accu_{y}\) values of 88.49% and 89.64%, respectively. Next, the LSTM and Bi-LSTM models reached closer \(accu_{y}\) values of 93.12% and 93.87%, respectively. Though the FD-CNN model resulted in a considerable \(accu_{y}\) of  
Figure 14: \(Accu_{y}\) analysis of the RFER-EADL approach and other modern algorithms. Figure 13: ROC curve analysis of the RFER-EADL algorithm. \begin{table}
\begin{tabular}{l c} \hline \hline
**Methods** & **Accuracy (\%)** \\ \hline RFER-EADL & 99.21 \\ LLDHF-FER & 88.49 \\ DSA-FER & 89.64 \\ FD-CNN & 94.35 \\ LSTM & 93.12 \\ Bi-LSTM & 93.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of RFER-EADL and other modern algorithms. 94.35%, the RFER-EADL model provided the maximum \(accu_{y}\) of 99.21%. These results confirm the enhanced FER outcomes of the RFER-EADL model. Figure 15 displays the training and testing accuracy analysis of the RFER-EADL technique applied to localization data. In testing and training accuracy, the proposed RFER-EADL model achieved superior performance. Notable is the fact that after 50 epochs, the accuracy values become saturated. Testing accuracy becomes much smaller than the training accuracy after the 20 th epochs. This means that our proposed model has higher performance in training and testing accuracy. Figure 16 displays a validation loss analysis of the RFER-EADL technique applied to localization data. In comparison to training loss, the Faster RCNN-DBMF approach minimized loss values. Notable is the fact that after 50 epochs, the loss values become saturated. Training loss becomes much smaller than the validation loss after the 20 th epochs. This means that our model has higher performance on the training dataset. Figure 15: RFER-EADL training and testing accuracy.