# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 4 Result and Discussion
### Discussion  
The results were quite satisfactory when the MobileNet model was evaluated using the Health Informatics dataset; however, the results were not gratifying for the other models. One of the reasons for this could be that the types of images that were utilized for training and testing were very different from one another. Although the Health Informatics dataset was in grayscale, the models were trained on RGB images. Because of this, the algorithms' capacity to appropriately classify the photos may have been impacted. It is possible that future research in this area will involve retraining the models using grayscale images or adjusting the models that have already been trained using transfer learning techniques in order to function with grayscale photos. Having said this, it is essential to bring attention to the fact that other variables could have also been responsible for the poor performance  
\begin{table}
\begin{tabular}{l l r} \hline
**Authors** & **Method** & **Accuracy Score** \\ \hline [PERSON] et al. [6] & [PERSON] & 85.62\% \\ \hline [PERSON] et al. [7] & SVM & 87\% \\ \hline [PERSON] et al. [9] & DenseNet & 86.71\% \\ \hline [PERSON] et al. [10] & RCNN & 60\% \\ \hline [PERSON] et al. [11] & MTCNN & 91\% \\ \hline Proposed Method & MN-KD & 92.75\% \\ \hline \end{tabular}
\end{table}
Table 6: Performance comparison with previous research works.  
of the models with the Health Informatics dataset. There may have been class imbalances or other problems in the dataset, which could have had an effect on the capacity of the models to generalize to new data [32].  
Further experiments with the models trained on grayscale images or with grayscale-specific pre-processing methods might be carried out to ascertain the extent to which the difference in color channels affected the performance of the models. This would make it easier to evaluate whether the color channels were the main cause of the subpar performance or whether other factors were involved. The existing system's requirement for suitable lighting conditions for video capturing is another drawback. The system may not be able to reliably detect yawns if the lighting is too low, which could have an impact on how well it works in general. Additionally, the device may malfunction in real-time applications if the user is wearing eyeglasses that restrict vision. Future research may examine the application of different methods, such as infrared cameras, to overcome these constraints and enhance the system's overall performance.  
In spite of the fact that the existing system has the capability to recognize drowsiness based on facial cues, there are a few problems that need to be fixed before it can achieve a higher level of accuracy and dependability in real-world settings. There may be situations when the device may not be able to capture the eyes of the driver, as is the case when the driver is wearing glasses, as shown in Figure 16. The challenge of detecting drowsiness when users wear sunglasses stems from the inability of cameras to capture eye movements accurately is depicted by the red boxes. Sunglasses obstruct visibility, hindering algorithms that rely on eye state analysis, which is critical for timely drowsiness detection.  
It is necessary to conduct additional research to investigate the utilization of a variety of visual modalities and the addition of additional characteristics in order to improve the overall performance of the system. Incorporating new features, such as audio or physiological information, into the system in order to improve its accuracy is another possible path that might be pursued in the work that will be conducted in the future. Changes in breathing patterns or heart rate, for instance, could also be indicative of drowsiness and could be used as additional characteristics to support the visual signals that come from the camera feed. This would complement the visual cues that come from the video feed.