# On-Road Driver Emotion Recognition Using Facial Expression
## 5 Results and Discussion
### Discussion  
The proposed FERDERnet applies transfer learning by fine-tune training the backbone network and combines face detection, crop, and image format transform image augmentation-based re-sampling to classify on-road driver emotions. The whole model is a novel method that achieves excellent recognition accuracy with insufficient and imbalanced data.  
As shown in Table 2, the experimental result of the proposed model obtained high accuracy with all five backbones (FERDERnet with Googlenet backbone achieved 89.8%  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **Precision** & **Recall** & **F1-Score** \\ \hline FERDERnet & _ours_ & 0.952 & 0.972 & 0.962 \\ FERDERnet(w/o) & _ours_ & 0.934 & 0.9 & 0.916 \\ \hline \hline \end{tabular} Note: _ours_ stands for on-road driver facial expression dataset.  
\end{table}
Table 5: Ablative evaluation with and without (w/o) the ABR module of FERDERnet.  
Figure 9: The confusion matrix of FERDERnet_X without ABR module when trained on the on-road driver facial expression dataset.  
top accuracy, which was the lowest among the five backbones). Apart from the model's effectiveness, the reason for the high top 1 accuracy of seven emotion classification is the lack of samples during data collection. Each subject did not necessarily show rich emotions in their facial expression; as a matter of fact, quite a few drivers rarely showed non-neutral emotions during the driving experiment. Due to this, the dataset did not contain a diversity of samples, which lowered the classification difficulty for the networks.  
Despite this fact, the proposed model still outperformed the baseline models (note that the dataset collected in this study contains driver facial expressions in night-driving) for 3.4% to 8.4% top 1 accuracy. In addition, the expression of different emotions was less identical than the lab controlled facial display, because drivers are highly focused on the road in the reality of driving.  
As the proposed network aims to apply a fast and accurate method for on-road driver facial expression recognition, recognition speed is essential. The best-trained model FERDERnet_X performed recognition at the speed of 12.8 FPS. However, image size significantly affected the system's speed, since the original image size is 1920 \(\times\) 1080. In practical applications, lower resolution images can improve the recognition speed. In future practical applications, the network needs to recognize the driver's emotion in different situations, including nighttime. The ABR module can be modified to perform image augmentation, enhance night image brightness, or adjust contrast in order to improve the emotion recognition accuracy.  
Under such circumstances, the result and ablative analysis demonstrate that the FERDERnet proposed in this study obtains significant improvements compare to the baseline networks. To the best of our knowledge, this is novel research in addressing on-road driver expression recognition, as this research also conducted experiments to collect on-road driver facial expressions induced by various scenarios.