# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 4 Experimental Results  
### CNN Validation  
Using Equations (2)-(5), the metrics were calculated. Table 3 presents the averages of each metric and its standard deviation corresponding to the validation in the training of the CNNs, seen in Section 3.4.  
As seen in Table 3, during the training validation, the CNN based on ResNet50V2 obtained a higher accuracy with \(99.89\%\pm 0.1\%\) average, followed by InceptionV3 with \(99.56\%\pm 0.1\%\) average accuracy and finally the one based on VGG16 with \(99.43\%\pm 0.1\%\) average accuracy. In the case of drowsiness detection, an important metric is the Recall, because the goal is to reduce as much as possible the false negatives of the Drowsy class. Thus, the CNN based on ResNet50V2 obtained the best average Recall with \(99.82\%\pm 0.2\%\).  
The comparative boxplot for the performance evaluation of CNNs in the training validation phase is presented in Figure 9, where the performance variations of Table 3 in the accuracy and recall metrics of the Drowsy class of each of the three networks in 10 training samples can be observed. In Figure 9a, the CNN based on ResNet50V2 presented a better performance in data validation with a median of \(99.9\%\) of the 10 training runs. Similarly, in Figure 9b, the ResNet50V2-based CNN had the best Recall performance of the Drowsy class with a median of \(99.8\%\), thus minimizing the false negatives of the Drowsy class.  
Figure 10 shows the radial behavior of the three CNNs, for the two metrics of the 10 trainings (experiments) performed. In Figure 10a, the CNN based on ResNet50V2 had a nearly constant behavior in the 10 experiments. On the other hand, the network based on VGG16 had a lower behavior compared to the other two networks, presenting a maximum accuracy of \(99.61\%\) in the last (10) experiment, and a minimum accuracy of \(99.12\%\) in the second experiment. Meanwhile, in Figure 10b, the CNN based on RedNest50V2 also presents a better performance compared to the other two networks. In the fifth experiment of this network, a \(99.41\%\) recall in the Drowsy class was obtained, being the minimum  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**CNN Based on** & **Class Name** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{2}{*}{InceptionV3} & Not drowsy & \(0.9945\pm 0.002\) & \(0.9967\pm 0.001\) & \(0.9956\pm 0.001\) & \multirow{2}{*}{\(0.9956\pm 0.001\)} \\  & Drowsy & \(0.9967\pm 0.001\) & \(0.9945\pm 0.002\) & \(0.9956\pm 0.001\) & \\ \hline \multirow{2}{*}{VGG16} & Not drowsy & \(0.9928\pm 0.002\) & \(0.9951\pm 0.003\) & \(0.9934\pm 0.001\) & \multirow{2}{*}{\(0.9934\pm 0.001\)} \\  & Drowsy & \(0.9951\pm 0.003\) & \(0.9928\pm 0.002\) & \(0.9934\pm 0.001\) & \\ \hline \multirow{2}{*}{ResNet50V2} & Not drowsy & \(0.9982\pm 0.002\) & \(0.9996\pm 0.001\) & \(0.9989\pm 0.001\) & \\  & Drowsy & \(0.9996\pm 0.001\) & \(0.9982\pm 0.002\) & \(0.9989\pm 0.001\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics in Training (Validation).  
Figure 9: Boxplot of two evaluation metrics for validation. Boxplots show median (solid line), minimum, maximum values with outliers shown as points. (**a**) Accuracy of the three networks. (**b**) Recall of Drowsy class of the three networks.  
value obtained, and likewise, the CNN based on VGG16 presented a lower performance in the recall of the Drowsy class.