# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 4 Results and Discussions
### Experiment 1
#### 4.1.3 Test Phase
The second observation was based on the test results after 100 epochs of training. The results of Test Model 1 are given in Table 12. Model 1's accuracy is increased after 100 epochs. All the emotions are detected above 88%. The most accurately detected emotion is happy with an accuracy of 95.14%, and the least accurately detected one is neutral. The overall accuracy is 91.82%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Anger \\ Disgust \\ Fear \\ \end{tabular} } & 1792 & 1474 & 318 & 82.25\% \\  & 2506 & 1829 & 677 & 72.98\% \\  & 1456 & 1254 & 202 & 86.13\% \\  & 3766 & 3485 & 281 & 92.54\% \\  & 2856 & 2336 & 420 & 81.79\% \\  & Sad & 3192 & 2970 & 222 & 93.05\% \\  & Surprise & 2842 & 2632 & 210 & 92.61\% \\  & Average & 18,410 & 15,980 & 2430 & 86.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Test result with prediction detail of the proposed model after 10 epochs. Figure 4: Confusion matrix of Test Model 2 after 10 epochs. Figure 5: Confusion matrix of the proposed model after 10 epochs. Test Model 2 is only capable of predicting the'surprise' emotion, as shown in Table 13. A 100% accuracy is obtained for surprise emotions while all other emotions are misclassified. The overall accuracy is 15.44%. According to the end results of the proposed model, the most accurate emotion recognized by the proposed model is'sad' at the rate of 95.21%, and the least is neutral emotion with 86.66%. All the emotions have an accuracy rate higher than 86%, as shown in Table 14. Figure 6, shows the confusion matrices after 100 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 & 0 & 1456 & 0\% \\ Happy & 3766 & 0 & 3766 & 0\% \\ Neutral & 2856 & 0 & 2856 & 0\% \\ Sad & 3192 & 0 & 3192 & 0\% \\ Surprise & 2842 & 2842 & 0 & 100\% \\ Average & 18,410 & 2842 & 15,568 & 15.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Test result with prediction detail of Model 2 after 100 epochs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Test Model 1 [21]** & **Test Model 2 [22]** & **Proposed Model** \\ \hline Anger & 68.14\% & 0\% & 82.25\% \\ Disgust & 78.73\% & 0\% & 72.98\% \\ Fear & 64.70\% & 100\% & 86.13\% \\ Happy & 93.36\% & 0\% & 92.54\% \\ Neutral & 73.49\% & 0\% & 81.79\% \\ Sad & 93.45\% & 0\% & 93.05\% \\ Surprise & 91.31\% & 0\% & 92.61\% \\ Average & 83.26\% & 7.91\% & 86.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of emotion prediction by Model 1, Model 2, and proposed model for experiment 1 after 10 epochs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 1792 & 1667 & 125 & 93.02\% \\ Disgust & 2506 & 2254 & 252 & 89.94\% \\ Fear & 1456 & 1321 & 135 & 90.73\% \\ Happy & 3766 & 3583 & 183 & 95.14\% \\ Neutral & 2856 & 2536 & 320 & 88.79\% \\ Sad & 3192 & 2901 & 291 & 90.88\% \\ Surprise & 2842 & 2642 & 200 & 92.96\% \\ Average & 18,410 & 16,904 & 1506 & 91.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Test result with prediction detail of Model 1 after 100 epochs. The confusion matrix for the proposed model after 100 epochs is presented in Figure 7. It indicates that the number of correct predictions is higher as compared to Test Model 1 and Test Model 2. As a result, the prediction accuracy is higher as a whole, as well as, for individual emotions. The proposed model accuracy achieves 92.66% accuracy after 100 epochs of training. It can be seen that the proposed method and Test Model 1 predict happy emotion with the same accuracy of 95.14%. However, the proposed model predicts other emotions more