## 3 Results
### Drowsiness Detection Classifier  
The results of the performed experiments, as shown in Table 4, show that the use of the additional metrics of PERCLOS, POM, or head pose compared to without the metrics has a positive effect on the classification accuracy. Where the use of both the landmarks and pairwise differences are used, Table 4 above shows that the use of PERCLOS, POM, and head pose positively affects the accuracy of each state of the eyes, mouth, and head, which in turn also helps the drowsiness detection since it is based on the results of each of these features. The use of PERCLOS and POM both show a notable improvement in both eye and mouth detection accuracy, while the head pose shows a significant enhancement in head detection accuracy across different models used. Although these improvements are promising, there may be potential limitations in real-world applications. In addition, the table above shows the different models used alongside the accuracies and sizes of the models that use PERCLOS, POM, and head pose for additional features to aid in drowsiness detection. The most suitable classifier for use in limited resource machines such as the Jetson Nano is XGBoost because of its relatively small model size and high accuracy, making it a highly balanced choice for both memory usage and performance. The SVM model may also be suitable owing to its high accuracy; however, its moderate size may become an issue. Despite its high accuracy, the Random Forest Neural Network model is not ideal for use in limited resource machines because there are storage constraints and higher computational costs. In addition, the previous methods in which others have \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Feature** & **Metric** & \begin{tabular}{c} **Without** \\ **Metric (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **With** \\ **Metric (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Difference (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{SVM} & Eyes & PERCLOS & 60.49 & 62.06 & +1.57 \\  & Mouth & POM & 86.22 & 87.24 & +1.02 \\  & Head & Head Pose & 86.50 & 91.69 & +5.19 \\ \hline \multirow{3}{*}{Random Forest} & Eyes & PERCLOS & 63.89 & 65.01 & +1.12 \\  & Mouth & POM & 88.01 & 89.93 & +1.92 \\  & Head & Head Pose & 88.86 & 94.44 & +5.58 \\ \hline \multirow{3}{*}{XGBoost} & Eyes & PERCLOS & 63.45 & 64.02 & +0.57 \\  & Mouth & POM & 88.67 & 89.73 & +1.06 \\ \cline{1-1}  & Head & Head Pose & 88.67 & 95.24 & +6.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy difference to check the relevance of handcrafted data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trial** & **Model** & **Region** & **Accuracy (\%)** & **Size (MB)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Include Facial \\ Landmarks and \\ Pairwise \\ Differences \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} SVM \\ Head \\ \end{tabular} } & Eyes & 62.06 & 13.73 \\  & & Mouth & 87.24 & 43.47 \\  & & Head & 91.69 & 90.05 \\ \cline{2-5}  & \multirow{4}{*}{\begin{tabular}{c} Random Forest \\ \end{tabular} } & Eyes & 65.01 & 156.66 \\  & & Mouth & 89.93 & 65.60 \\  & & Head & 94.44 & 55.35 \\ \cline{2-5}  & \multirow{4}{*}{\begin{tabular}{c} XGBoost \\ \end{tabular} } & Eyes & 64.02 & 0.69 \\  & & Mouth & 89.73 & 2.19 \\  & Head & 95.24 & 2.05 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Without Facial \\ Landmarks \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} SVM \\ Head \\ \end{tabular} } & Eyes & 56.62 & 36.62 \\  & & Mouth & 87.05 & 37.27 \\  & Head & 90.59 & 89.87 \\ \cline{1-1} \cline{2-5}  & \multirow{4}{*}{\
\begin{tabular}{c} Random Forest \\ \end{tabular} } & Eyes & 63.48 & 149.43 \\  & & Mouth & 89.72 & 67.84 \\ \cline{1-1}  & Head & 94.45 & 55.58 \\ \cline{1-1} \cline{2-5}  & \multirow{4}{*}{
\begin{tabular}{c} XGBoost \\ \end{tabular} } & Eyes & 63.33 & 0.68 \\ \cline{1-1}  & & Mouth & 89.48 & 2.20 \\ \cline{1-1}  & Head & 95.20 & 2.06 \\ \hline \hline \end{tabular}
\end{table}