# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 4 Results and Discussions
### Experiment 2
#### 4.2.3 Testing Phase  
In the testing session of experiment 2, we use the JAFFE database for each model as the testing dataset. The first observation is based on training setup up to only 10 epochs. To perform this first, we feed the dataset in trained models and check the accuracy. The results of Test Model 1, given in Table 19, highlight the overall accuracy of this model which is 84.17%. It can identify all emotions but the most accurate one is'surprise' with an accuracy rate of 95.48% and neutral emotions has the lowest accuracy of 50.71%. However, angry, happy, and sad emotions are above 84% rate. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Test Model 1 [21]** & **Test Model 2 [22]** & **Proposed Model** \\ \hline Overall accuracy & 85.88\% & 18.93\% & 89.76\% \\ Validation accuracy & 84.54\% & 20.37\% & 85.64\% \\ Overall loss & 0.47114 & 18.6617 & 0.29435 \\ Validation loss & 0.44943 & 18.33486 & 0.44149 \\ Time per epoch & 35.641/s & 54.904/s & 41.302/s \\ \hline \hline \end{tabular}
\end{table} 
Table 17: First observation after 10 epochs of training of experiment 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 420 & 354 & 66 & 84.29\% \\ Disgust & 406 & 387 & 19 & 95.32\% \\ Fear & 448 & 341 & 107 & 76.12\% \\ Happy & 434 & 413 & 21 & 95.16\% \\ Neutral & 420 & 213 & 207 & 50.71\% \\ Sad & 434 & 401 & 33 & 92.40\% \\ Surprise & 420 & 401 & 19 & 95.48\% \\ Average & 2982 & 2510 & 472 & 84.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Cross dataset testing result of Test Model 1 after 10 epochs. Test Model 2 is only able to detect the 'anger' emotion with 100% accuracy while all other emotions are incorrectly predicted, as shown in Table 20. The overall accuracy of the model is 14.08% only. According to the results given in Table 21, the overall accuracy of the proposed work is 84.27% where the highly accurate emotion is'sad' with 97.93% and the lowest accuracy is for 'neutral' emotion, i.e., 59.52%. However, all other emotions i.e., angry, disgust, fear, happy and surprise emotions high an accuracy higher than 80%. Figure 8 shows the confusion matrix for the proposed model. Performance comparison of all models after 10 epochs is given in Table 22 which indicates that Test Model 2 performs poorly. Test Model 2 performs well; however, the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 420 & 420 & 0 & 100\% \\ Disgust & 406 & 0 & 406 & 0\% \\ Fear & 448 & 0 & 448 & 0\% \\ Happy & 434 & 0 & 434 & 0\% \\ Neutral & 420 & 0 & 420 & 0\% \\ Sard & 434 & 0 & 434 & 0\% \\ Surprise & 420 & 0 & 420 & 0\% \\ Average & 2982 & 420 & 2562 & 14.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Cross dataset testing result of Model 2 after 10 epochs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotions** & **Dataset** & **Correct Prediction** & **Incorrect Prediction** & **Percentage** \\ \hline Anger & 420 & 337 & 83 & 80.24\% \\ Disgust & 406 & 371 & 35 & 91.38\% \\ Fear & 448 & 362 & 86 & 80.80\% \\ Happy & 434 & 362 & 72 & 83.41\% \\ Neutral & 420 & 250 & 170 & 59.52\% \\ Sad & 434 & 425 & 9 & 97.93\% \\ Surprise & 420 & 406 & 14 & 96.67\% \\ Average & 2982 & 2513 & 469 & 84.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Cross dataset testing result of the proposed model after 10 epochs. Figure 8: Confusion matrix of the proposed model performed with cross dataset after 10 epochs. performance of the proposed model is marginally better than Test Model 2 with 84.27% accuracy. After the satisfactory results from test 1 with the cross dataset, a second test is performed which is based on one 100 epochs. After training all the models up to 100 epochs, data is tested on each model and obtain the following outcomes.