# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 4 Results and Discussions
### Discussions  
To compare the results, the precision of all models is considered. Precision is calculated with the following equation:  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
Table 27 tells us about the comparison between models and experiments of observation 1 for experiment 1. According to the given results, the average precision of the proposed model is higher (0.8578) than other models (0.8372 for Test Model 1 and 0.0113 for Test Model 2). For experiment 2, observation 1, the proposed model has an average precision of 0.8499, whereas Test Model 1 has 0.8488 precision, as shown in Table 28. Test Model 2 shows the lowest precision in both experiments. In experiment 1, the proposed model is able to predict five emotions, disgust, happy, neutral, sad, and surprise, more precisely than other models. A similar trend is observed in the case of experiment 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Test Model 1 [21]** & **Test Model 2 [22]** & **Proposed Model** \\ \hline Anger & 93.33\% & 0\% & 96.43\% \\ Disgust & 97.29\% & 0\% & 96.31\% \\ Fear & 97.10\% & 0\% & 96.88\% \\ Happy & 94.93\% & 0\% & 94.93\% \\ Neutral & 76.19\% & 0\% & 84.29\% \\ Sad & 97.24\% & 100\% & 100\% \\ Surprise & 95.95\% & 0\% & 95.48\% \\ Average & 93.19\% & 14.55\% & 94.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Comparison of Test Model 1, Test Model 2, and proposed model after 100 epochs with cross dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 420 & 405 & 15 & 96.43\% \\ Disgust & 406 & 391 & 15 & 96.31\% \\ Fear & 448 & 434 & 14 & 96.88\% \\ Happy & 434 & 412 & 22 & 94.93\% \\ Neutral & 420 & 354 & 66 & 84.29\% \\ Sad & 434 & 434 & 0 & 100\% \\ Surprise & 420 & 401 & 19 & 95.48\% \\ Average & 2982 & 2831 & 151 & 94.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Cross dataset testing result of the proposed model after 100 epochs. Observation 2 of both experiments is given in Tables 29 and 30, respectively. It can be observed that the precision of the proposed model is higher than other models. The average precision of the proposed model in experiment 1 is 0.9235, and that of Test Model 1 is 0.9186. In experiment 2, the proposed model has a precision of 0.9614 and Test Model 1 has a precision of 0.9320. Test Model 2 has a precision of 0.0221 and 0.0208, respectively. In observation 2 of experiment 1, the proposed model is more precise for emotions of anger, disgust, happy and neutral whereas, in experiment 2, disgust fear neutral, sad, and surprise is more accurate than the others. The proposed model takes a slightly longer time than Test Model 1 but it is faster than Test Model 2 and more accurate than both models. It achieved up to 92.66% accuracy in experiment 1 and 94.94% in experiment 2 based on the cross dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**Emotions** & **Test Model 1 [21]** & **Test Model 2 [22]** & **Proposed Model** \\ \hline Anger & 0.8392 & 0.0000 & 0.7916 \\ Disgust & 0.8385 & 0.0000 & 0.9375 \\ Fear & 0.8904 & 0.0791 & 0.7642 \\ Happy & 0.8584 & 0.0000 & 0.9378 \\ Neutral & 0.7342 & 0.0000 & 0.7781 \\ Sad & 0.8193 & 0.0000 & 0.9016 \\ Surprise & 0.8803 & 0.0000 & 0.8940 \\ Average & 0.8372 & 0.0113 & 0.8578 \\ \hline \end{tabular}
\end{table} 
Table 27: Precision comparison between all three models with respect to observation 1 of Experiment 1 with epochs 10. \begin{table}
\begin{tabular}{c c c c} \hline
**Emotions** & **Test Model 1 [21]** & **Test Model 2 [22]** & **Proposed Model** \\ \hline Anger & 0.8806 & 0.1408 & 0.9493 \\ Disgust & 0.6661 & 0.0000 & 0.7894 \\ Fear & 0.9394 & 0.0000 & 0.8599 \\ Happy & 0.8463 & 0.0000 & 0.9731 \\ Neutral & 0.7448 & 0.0000 & 0.7463 \\ Sad & 0.8911 & 0.0000 & 0.7430 \\ Surprise & 0.9733 & 0.0000 & 0.8884 \\ Average & 0.8488 & 0.0201 & 0.8499 \\ \hline \end{tabular}
\end{table}