## 3 Results
### Drowsiness Detection Classifier
This study demonstrates the potential of using simpler yet effective machine learning models, such as XGBoost, to achieve a balance between accuracy and computational efficiency, paving the way for the development of practical drowsiness detection systems that can be deployed on a wider range of devices. Figure 4 shows sample images taken from the webcam simulation, which on the top left of the display shows the frames per second of the stream along the orientation of the head visualized from the three Euler angles of yaw, pitch, and roll retrieved from the HOPEnet model. In addition, the proposed method achieved an average of 16 fps. The top right of the display shows the frontalized landmarks; the bottom left of the display shows the yaw, pitch, and roll; and the bottom right of the display shows the prediction of each region state and its confidence scores. The display also shows the face detected in the frame using a bounding box and 68-point landmarks on the face from Dlib. Table 7 shows a comprehensive comparison between the proposed method and other studies on drowsiness detection. Here, the HTDBN model is the closest comparison to the proposed method in terms of accuracy, where the proposed method using XGBoost shows an overall accuracy of 83.96% compared to the method performed using HTDBN [12] with an accuracy of 84.82%. The two accuracies are not far apart, with only a difference of 0.86%; however, the proposed method was able to achieve a total model size of only 5.05 MB compared to the larger 37.82 MB of the HTDBN. This size difference may limit their practicality in low-computing devices. Although research on using HTDBN for drowsiness \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Feature** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{SVM} & Eyes & 0.68 & 0.61 & 0.51 \\  & Mouth & 0.89 & 0.87 & 0.75 \\  & Head & 0.90 & 0.91 & 0.62 \\  & Drowsiness & 0.96 & 0.95 & 0.95 \\ \hline \multirow{4}{*}{Random Forest} & Eyes & 0.65 & 0.63 & 0.63 \\  & Mouth & 0.89 & 0.78 & 0.82 \\  & Head & 0.94 & 0.74 & 0.82 \\  & Drowsiness & 0.96 & 0.95 & 0.95 \\ \hline \multirow{4}{*}{XGBoost} & Eyes & 0.66 & 0.62 & 0.61 \\  & Mouth & 0.89 & 0.78 & 0.82 \\ \cline{1-1}  & Head & 0.94 & 0.79 & 0.85 \\ \cline{1-1}  & Drowsiness & 0.96 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of experiments using SVM, Random Forest, and XGBoost. Figure 4: Sample pictures taken from webcam simulation. (**a**) Sleepy eyes and yawning are detected as a drowsy subject; (**b**) stillness of eyes and stillness of mouth while head is looking aside are detected as not drowsy. detection [12] did not mention the size of the model, after performing experimentation, a size of 37.82 MB was achieved. This figure is a rough estimate based on replicating the HTDBN model as closely as possible to the descriptions provided in the study. It is important to note that this size may vary depending on the specific implementation details of Deep Belief Networks (DBN) and Hidden Markov Models (HMM), such as the number of layers, number of units in each layer, and precision of the weights used. Because the original paper did not provide explicit details on these aspects, the estimated size serves as an approximation that reflects the potential scale of the HTDBN model based on standard practices in model construction and optimization. Other methods such as KCF + Dlib [16], as well as using U-Net + Decision Network [19], achieved high accuracies of 92% and 96.72%, respectively; however, they did not specify the model sizes. The KCF + Dlib method relies primarily on facial features and the KCF algorithm for drowsiness detection, which may not provide a comprehensive assessment of drowsiness. Similarly, the U-Net + Decision Network [19] approach focuses solely on eye closure, potentially overlooking other important indicators of drowsiness. Moreover, the CNN + Facial Landmark Detection method [15] offers a balanced approach with an accuracy of 83.33% and a minimal model size of 0.075 MB.