# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 4 Results and Discussions
### Experiment 1
#### 4.1.3 Test Phase  
To test the models, the previously created testing dataset is used for each model. Feeding the dataset into the model, Table 8 stats are obtained as follows. The first test is performed after 10 epochs to check the model's accuracy on that point and confirm that the model is correct and capable of prediction. After 10 epochs, the accuracy rate of Model 1 is 83.26%. The most accurate emotion projected by Model 1 is'sad' with 93.45% precision and the least accurate emotion is fear at the rate of 68.70%. However, happy, sad, and surprise emotions are above 90% accurately predicted as seen in Table 8. Then, a confusion matrix is created, as shown in Figure 3, where the accuracy of testing exceeding the comparable is clearly seen. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Test Model 1**[21] & **Test Model 2**[22] & **Proposed Model** \\ \hline Overall accuracy & 86.33\% & 17.2\% & 91.23\% \\ Validation accuracy & 83.74\% & 16.63\% & 86.57\% \\ Overall loss & 0.43776 & 19.06535 & 0.28740 \\ Validation loss & 0.46539 & 19.19623 & 0.40479 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: First observation after 10 epochs of training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Test Model 1**[21] & **Test Model 2**[22] & **Proposed Model** \\ \hline Overall accuracy & 98.33\% & 16.29\% & 99.44\% \\ Validation accuracy & 92.33\% & 15.40\% & 93.20\% \\ Overall loss & 0.04361 & 19.27471 & 0.01304 \\ Validation loss & 0.44888 & 19.48092 & 0.38968 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Second observation of the first experiment after 100 epochs of training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 1792 & 1221 & 571 & 68.14\% \\ Disgust & 2506 & 1973 & 533 & 78.73\% \\ Fear & 1456 & 942 & 514 & 64.70\% \\ Happy & 3766 & 3516 & 250 & 93.36\% \\ Neutral & 2856 & 2099 & 757 & 73.49\% \\ Sad & 3192 & 2983 & 209 & 93.45\% \\ Surprise & 2842 & 2595 & 247 & 91.31\% \\ Average & 18,410 & 15,329 & 3081 & 83.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Test result with prediction detail of Model 1 after 10 epochs. Test Model 2 only predicted the fear emotion with 100% accuracy and all the other emotions were mispredicted. The overall accuracy of the model is 7.91% as shown in Table 9. Figure 4 shows the number of correct and wrong predictions from Test Model 2 after 10 epochs. It can be seen that all emotions are misclassified except for fear. The accuracy of the proposed model is 86.80%, as shown in Table 10. The most accurate emotion predicted by the proposed model is'sads at the rate of 93.05% and the least accurate emotion is 'disgusts with a 72.98% accuracy rate. Happy, sad, and surprise emotions are predicted above 92%. Figure 5 shows the confusion matrix of the proposed approach. Results indicate that its performance is better than both Test Model 1 and Test Model 2 on average, as it produces a higher number of correct emotions. The comparison of all three models can be seen in Table 11. After 10 epochs, the accuracy rate of Model 1 is 83.26%, Model 2 is 7.91% and the proposed model is 86.80%, which is the highest as compared to other models. The overall accuracy of the projected model is greater than other models. Figure 3: Confusion matrix of Test Model 1 after 10 epochs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 1792 & 0 & 1792 & 0\% \\ Disgust & 2506 & 0 & 2506 & 0\% \\ Fear & 1456 & 1456 & 0 & 100\% \\ Happy & 3766 & 0 & 3766 & 0\% \\ Neutral & 2856 & 0 & 2856 & 0\% \\ Sad & 3192 & 0 & 3192 & 0\% \\ Surprise & 2842 & 0 & 2842 & 0\% \\ Average & 18,410 & 1456 & 16,954 & 7.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test result with prediction detail of Model 2 after 10 epochs. As the first test results were satisfactory, a second test was performed.