# On-Road Driver Emotion Recognition Using Facial Expression
## 5 Results and Discussion
### Performances  
The dataset this study adopted to validate the performance for all baseline methods and FERDERnet with different backbones was the on-road driver facial expression dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **Specificity** & **F1-Score** & **ACC** & **Training Time** \\ \hline Adam & 0.941 & 0.96 & 0.991 & 0.951 & 0.953 & 2303.3 s \\ SGD & 0.471 & 0.469 & 0.917 & 0.4656 & 0.575 & 2284.8 s \\ RMSprop & 0.941 & 0.949 & 0.991 & 0.945 & 0.952 & 2297.8 s \\ Adagrad & 0.706 & 0.685 & 0.951 & 0.695 & 0.748 & 2296.2 s \\ Adamax & 0.944 & 0.943 & 0.988 & 0.943 & 0.948 & 2320.8 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different optimization algorithms for FERDERnet. Figure 5: Driver facial expressions in gray images processed by the FD module. To evaluate the performance of the network, the results obtained were reported using accuracy, precision, recall, and F1-score. Table 2 demonstrates the performance between each baseline network and the corresponding FERDERnet that applies the same network for its backbone. Among the five backbones employed in this experiment, FERDERnet_G (FERERnet with Googlenet backbone) reached a classification top 1 accuracy of 88.8%, higher than Googlenet (top 1 accuracy: 80.3%) by 8.5%. Furthermore, as can be seen, the F1-score of FERDERnet_G is also significantly higher than the Googlenet, by 9.9%. For each backbone adopted, the table reveals that the proposed FERDERnet achieved remarkably high emotion classification accuracy, as each FERDERnet exceeded its baseline for the top 1 accuracy by 3.3% to 8.3%. It is clear that the samples in the collected on-road driver facial expression dataset are all of the same nationality so that all methods meet a reasonably high classification performance. Despite that, the FERDERnet still surpasses the baseline networks. Another comparative study can be seen in Table 2. FERDERnet with different backbone networks achieves a diverse top 1 accuracy range from 88.8% to 96.6%. The FERDERnet_X (FERERnet with Xception backbone) achieved the best classification top 1 accuracy of 96.6%. For further discussion, the F1-score plays an important role when evaluating multi-class classification tasks on imbalanced data. Hence, the performance of the methods proposed was also assessed on F1-score. The Table shows that FERDERnet_X achieved the highest F1-score of 0.962. The results indicate that FERDERnet_X performs best among the backbones applied. Figure 6: Results of different training algorithms. The above charts present training loss (blue line) and test accuracy (orange line); the bottom charts present the corresponding confusion matrix. (**a**) ACC and Loss Adam, (**b**) ACC and Loss SGD, (**c**) ACC and Loss RMSprop, (**d**) ACC and Loss Adagrad, (**e**) ACC and Loss Adamax, (**f**) matrix Adam, (**g**) matrix SGD, (**h**) matrix RMSprop, (**i**) matrix Adagrad, (**j**) matrix Adamax. As shown in Figure 7, the confusion matrix of FERDERnet_X performed significantly well in seven-class emotion classification. The model performed pretty well in classes such as Angry, Happy, and Sad, while performing slightly worse in Fear and Surprise. This may be caused by class imbalance because the samples are still not fully balanced after the ABR module (Fear contained 464 images, Surprise contained 642 images, Neutral contained 6265 images). Overall, the proposed FERDERnet with Xception backbone obtained excellent classification accuracy for the on-road driver facial expression recognition task. The proposed network FERDERnet_X performed best among the backbones applied. To further evaluate the method compared to other state-of-the-art networks in the facial expression recognition task, some robust networks--DeepEmotion [38], ARM [39], VGGNet [48], ResMaskingNet [37], Resnet [73], Inception [74]--and FERDERnet with an ensemble of five backbone networks were trained on our on-road driver facial expression dataset under the same training strategy.