## 4 Results
### Results of Driver Fatigue Detection  
We randomly selected five men and five women from the YawDD data set for the experiments. The accuracies of face detection, eye detection, eye opening and closing detection using a CNN, and the eye opening and closing using the EAR metric are shown in Table 1. The average accuracies were 100% for both face and eye detection. Moreover, the average accuracies of eye opening and closing detection using a CNN and the EAR metric were 93.5% and 94.5%, respectively. Hence, the EAR metric performed better than a CNN when used to detect eye opening and closing.  
Figure 5 indicated the detection of eye opening and closing using the CNN and EAR metrics. The orientation of the face was also reflected in the direction of head posture.  
In the classification of driver fatigue, 20% of all 53 videos in the YawDD data set were used for testing and the rest were used for training. We used the random forest algorithm to classify and analyze driver fatigue. The results are shown in Table 2. The number of trees and the minimum number of samples in a leaf were set as 10 and 1, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Video \#** & **Total** & **Face** & **Eye** & **O/C CNN** & **O/C EAR** \\ \hline
29-male & 644 & 100\% & 100\% & 89.5\% & 94.4\% \\
34-male & 645 & 100\% & 100\% & 100\% & 98.6\% \\
40-male & 638 & 100\% & 100\% & 100\% & 90.1\% \\
42-male & 643 & 100\% & 100\% & 100\% & 93.2\% \\
45-male & 641 & 100\% & 100\% & 94.6\% & 94.3\% \\
12-female & 639 & 100\% & 100\% & 90.5\% & 97.3\% \\
13-female & 779 & 100\% & 100\% & 90.6\% & 96.4\% \\
35-female & 642 & 100\% & 100\% & 99.3\% & 96.3\% \\
37-female & 646 & 100\% & 100\% & 82.2\% & 93.7\% \\
41-female & 432 & 100\% & 100\% & 86.1\% & 87.3\% \\ All/Average & 6349 & 100\% & 100\% & 93.5\% & 94.5\% \\ \hline \end{tabular}
\end{table}
Table 1: Detection accuracy. Total: total number of images. Face: face detection accuracy. Eye: eye detection accuracy. O/C CNN: detection accuracy of eye open/close using CNN. O/C EAR: detection accuracy of eye open/close using EAR.  
Figure 5: Detection of eye opening and closing using a CNN (**left**) and the EAR metric (**right**). The images also indicated the orientation of the face in the detection of head posture.  
The importance of each fatigue parameter was shown in Table 3. The results revealed that the frequency of yawning was the most crucial parameter for detecting driver fatigue because it was highly related to the use of the YawDD data set for training. The second and third most crucial parameters were blink frequency and PERCLOS, respectively.  
We compared our approach in detecting driver fatigue with its counterparts in the literature [20; 21; 35; 36]. Table 4 shows the comparison of our approach with previous methods. At 91%, the proposed approach is more accurate than those of [PERSON] et al. [20], at 88.6%, [PERSON] and [PERSON] [21], at 83%, [PERSON] et al. [35], at 79.8%, and [PERSON] and [PERSON] [36], at 85.6%.  
Our approach was also fast. It processed an image in 0.47-0.5 s on the PC (including approximately 0.24 s for the random forest computation) and could output a driver fatigue detection result in 2.8 s on the Jetson TX2 platform.