# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 4 Results and Discussions
### Experiment 2
#### 4.2.2 Training Phase  
For training, we use the same method as in the previous experiment except for the dataset. In the testing phase, we feed the CK+ dataset, train the model, and analyze the behavior of the models. Initially, the results are checked after the completion of training up to 10 epochs. The preliminary observation was the same as before in the experiment. The proposed model took longer time than Test Model 1 but the accuracy of the model is superior to the other two models. Test Model 1 is the fastest of all but not as precise as the proposed model. Test Model 2 is neither fast nor accurate as compared to any other model.  
Table 17 shows the experimental results after 10 epochs. The accuracy of the proposed Model is 89.76%, and the loss is 0.29435 where Test Model 1 has 85.88% accuracy, and loss is 0.47114 and Test Model 2 gets only 18.93% accuracy and loss is 18.6617. The time of the proposed model to complete each epoch is 41.302 s where Test model 1 finishes one epoch in 35.641 s. Test Model 2 is the slowest of all with 54.904 s. It can be seen that the proposed model has higher accuracy and the least loss as compared to others.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Total** & **Split** & **Percentage** & **\# of Images** & \
\begin{tabular}{c} **Total Images after** \\ **Preprocessing** \\ \end{tabular} \\ \hline CK+ & 6362 &
\begin{tabular}{c} Training \\ Validation \\ \end{tabular} & 50\% & 3181 & 44,534 \\ \hline JAFFE & 213 & Testing & 100\% & 213 & 2982 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Division detail along with numbers of images per set for cross dataset test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotions** & **Test Model 1[21]** & **Test Model 2[22]** & **Proposed Model** \\ \hline Anger & 93.02\% & 0\% & 93.14\% \\ Disgust & 89.94\% & 0\% & 90.66\% \\ Fear & 90.73\% & 0\% & 92.99\% \\ Happy & 95.14\% & 0\% & 95.14\% \\ Neutral & 88.79\% & 0\% & 86.66\% \\ Sad & 90.88\% & 0\% & 95.21\% \\ Surprise & 92.96\% & 100\% & 93.81\% \\ Average & 91.82\% & 15.44\% & 92.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of emotion prediction by Model 1, Model 2, and proposed model for experiment 1 after 100 epochs.  
Similar to the first experiment, the second training session is performed up to 100 epochs to evaluate models. The results of the training are shown in Table 18. On 100 epochs, the total number of iterations is 44,534 and the total number of steps is 69,600 during training. After the training, the proposed model achieves a 99.40% accuracy and 93.83% overall accuracy. The loss is dropped to 0.01603 and validation loss reaches 0.35530 in time of 35.817 s/epoch.  
Test Model 1 obtains 98.34% accuracy and 92.83% validation accuracy. The loss rate is reduced to 0.03960 and the validation loss becomes 0.42293 within 30.463 s/epoch, whereas Test Model 2 only manages to reach the accuracy of 16.11% with a validation accuracy of 17.06%. The loss rate of this model is 19.31624 and the validation loss is 19.09687 in 47.763 s/epoch time.