## 3 Results
### Drowsiness Detection Classifier

Table 3: Difference in using both facial landmarks and pairwise differences compared to only using pairwise differences. tried to use heavier models, such as CNN-based methods, Hierarchical Temporal Deep Belief Networks (HTDBN), and RCAN, although their accuracies are much larger, also have larger model sizes. Owing to these larger model sizes, testing could not be performed on machines with limited resources. Furthermore, from the data in Table 5, it is also possible to obtain the overall accuracies using the SVM, Random Forest, or XGBoost models. The Support Vector Machine (SVM) model, with an overall accuracy of 82.44%, shows its capability to effectively interpret the data at the cost of relatively large model sizes, especially noticeable in the head feature analysis with a size of 90.05 MB. The model size, measured in megabytes (MB), indicates the storage space required for the serialized model file. It is the size of the trained model that is created in the simulation. This suggests a trade-off between the accuracy and resource consumption for the SVM model. The Random Forest model, with an overall accuracy of 84.06%, not only slightly outperforms the SVM in accuracy but also shows a significant variance in model sizes, ranging from a modest 0.18 MB for drowsy state detection to a substantial 156.66 MB for eye state classification. This indicates its robustness but also points to potential challenges in deployment in devices with limited storage capacities. XGBoost, with an overall accuracy of 83.96%, showed not only competitive accuracy, particularly in head movement analysis but also the smallest model sizes compared to the other models, with the drowsy state detection model being notably compact at 0.12 MB. This size efficiency makes XGBoost particularly appealing for applications in environments with strict constraints on computational resources. The comparison of these models highlights the importance of balancing accuracy with model size, especially for real-world applications where both factors are crucial for the deployment of drowsy driving detection systems. As shown by Table 6, the macro-averaged precision, recall, and F1-score results for SVM, Random Forest, and XGBoost showed strong performance in drowsiness detection, with each model achieving high scores in this category. However, there are variations in performance across different facial features, with SVM excelling in mouth detection but facing challenges in eye and head-state classification. Random Forest demonstrates a balanced performance across features, particularly in mouth and head detection. XGBoost shows an improvement in head feature classification compared with the other models. Overall, these results indicate the effectiveness of these models in detecting drowsiness while highlighting areas for improvement in specific feature classifications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Region** & **Accuracy (\%)** & **Model Size (MB)** \\ \hline \multirow{4}{*}{SVM} & Eyes & 62.06 & 13.73 \\  & Mouth & 87.24 & 43.47 \\  & Head & 91.69 & 90.05 \\  & Drowsiness & 88.77 & 0.32 \\ \hline \multirow{4}{*}{Random Forest} & Eyes & 65.01 & 156.66 \\  & Mouth & 89.93 & 65.60 \\ \cline{1-1}  & Head & 94.44 & 55.35 \\ \cline{1-1}  & Drowsiness & 86.85 & 0.18 \\ \hline \multirow{4}{*}{XGBoost} & Eyes & 64.02 & 0.69 \\ \cline{1-1}  & Mouth & 89.73 & 2.19 \\ \cline{1-1}  & Head & 95.24 & 2.05 \\ \cline{1-1}  & Drowsiness & 86.85 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimentation results using the three classifiers of SVM, Random Forest, and XGBoost. Comparing these results with previous methods that employed heavier models, such as CNN-based methods, HTDBN, and RCAN, it is evident that while those approaches may offer higher accuracies, their larger model sizes limit their applicability in resource-constrained environments.