# Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model
## 4 Results and Discussion  
The experimental justification of the REFER-EADL technique took place using the CK+ dataset [26], which holds 837 images under seven class labels, as depicted in Table 2. Figure 4 shows some sample images. The FER-2013 dataset's training set has 28,000 tagged images. The development set includes 3500 tagged photos, whereas the test set includes 3500 images. FER-2013 labels each image with one of seven emotions: joyful, sad, angry, terrrified, astonished, disgusted, or neutral. Figure 6 illustrates the FER results of the RFER-EADL model on 70% of the TR dataset. The results suggest that the RFER-EADL technique recognized all facial expressions accurately. For instance, in class A, the RFER-EADL model offered \(accu_{y}\) of 99.32%, \(sens_{y}\) of 93.33%, \(spec_{y}\) of 99.64%, _an \(F_{score}\)_ of 93.33%, and an MCC of 92.97%. Additionally, for class Co, the RFER-EADL technique rendered \(accu_{y}\) of 98.63%, \(sens_{y}\) of 46.15%, \(spec_{y}\) of 99.83%, an \(F_{score}\) of 60%, and an MCC of 62.33%. Moreover, for class Di, the RFER-EADL technique  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Labels** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** & **MCC** \\ \hline \multicolumn{6}{l}{**Training Validation (70\%)**} \\ \hline An & 99.32 & 93.33 & 99.64 & 93.33 & 92.97 \\ \hline Co & 98.63 & 46.15 & 99.83 & 60.00 & 62.33 \\ \  
granted \(accu_{y}\) of 98.97%, \(sens_{y}\) of 93.02%, \(spec_{y}\) of 99.45%, an \(accu_{y}\) of 93.02%, and an MCC of 92.47%. Figure 7 exemplifies the FER results of the RFER-EADL on 30% of the TS dataset. The outcomes denoted by the RFER-EADL approach recognized all facial expressions precisely. For example, for class A, the RFER-EADL technique achieved presented \(accu_{y}\) of 100%, \(sens_{y}\) of 100%, \(spec_{y}\) of 100%, an \(F_{score}\) of 100%, and an MCC of 100%. Likewise, for class Co, the RFER-EADL technique rendered \(accu_{y}\) of 99.60%, \(sens_{y}\) of 80%, \(spec_{y}\) of 100%, an \(F_{score}\) of 88.89%, and an MCC of 89.26%. Further, on class Di, the RFER-EADL technique provided \(accu_{y}\) of 99.21%, \(sens_{y}\) of 93.75%, \(spec_{y}\) of 99.58%, an \(F_{score}\) of 93.75%, and an MCC of 93.33%. Figure 6: Average analysis of the RFER-EADL algorithm under 70% of TR dataset. Figure 7: Average analysis of RFER-EADL algorithm under 30% of the TS dataset. Table 4 establishes the overall FER results of the RFER-EADL approach on 20% of TS data and 80% of TR data. Figure 8 shows the FER results of the RFER-EADL technique on 80% of TR data. The effects specify that the RFER-EADL algorithm recognized all facial expressions accurately. For example, for class A, the RFER-EADL methodology provided \(accu_{y}\) of 99.10%, \(sens_{y}\) of 89.74%, \(spec_{y}\) of 99.68%, an \(F_{score}\) of 92.11%, and an MCC of 91.67%. Additionally, for class Di, the RFER-EADL approach provided \(accu_{y}\) of 98.95%, \(sens_{y}\) of 91.11%, \(spec_{y}\) of 99.52%, an \(F_{score}\) of 92.13%, and an MCC of 91.58%. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Labels** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** & **MCC** \\ \hline \multicolumn{6}{l}{**Training Phase (80\%)**} \\ \hline An & 99.10 & 89.74 & 99.68 & 92.11 & 91.67 \\ \hline Co & 99.25 & 66.67 & 100.00 & 80.00 & 81.34 \\ \hline Di & 98.95 & 91.11 & 99.52 & 92.13 & 91.58 \\ \hline Fe & 99.40 & 75.00 & 100.00 & 85.71 & 86.34 \\ \hline Ha & 98.21 & 89.66 & 99.02 & 89.66 & 88.67 \\ \hline Nu & 97.31 & 99.36 & 92.39 & 98.12 & 93.50 \\ \hline Sa & 99.10 & 87.50 & 99.53 & 87.50 & 87.03 \\ \hline Average & 98.76 & 85.58 & 98.59 & 89.32 & 88.59 \\ \hline \multicolumn{6}{l}{**Testing Phase (20\%)**} \\ \hline An & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline Co & 99.40 & 100.00 & 99.39 & 85.71 & 86.34 \\ \hline Di & 98.81 & 100.00 & 98.70 & 93.33 & 92.93 \\ \hline Fe & 98.21 & 66.67 & 100.00 & 80.00 & 80.89 \\ \hline Ha & 98.8 Figure 9 demonstrates the FER results of the RFER-EADL technique on 20% of the TS data.