# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 5 Conclusions  
The study successfully illustrated the ability of deep learning models in detecting driver drowsiness via facial analysis, with MobileNet appearing as the most advantageous model due to its superior accuracy of 92.75% with the testing dataset and 91.98% with the previously unexplored Health Informatics dataset. Its lightweight structure and great computing efficiency make it ideal for real-time applications in resource-constrained environments. While the CNN model generated hopeful results with an intermediate accuracy and efficiency, the VGG16 model underperformed, with a low accuracy and restricted generalizability. These findings underscore the significance of adopting models that strike a compromise between accuracy, computing economy, and adaptability for real-world use. MobileNet's capacity to handle various datasets and its robustness in detecting minor facial cues underline its supremacy. However, challenges such as poor illumination, obstructions such as eyeglasses, and image format inconsistencies exist. Future research should over  
Figure 16: Challenges in detecting drowsiness.  
come these limits by retraining models using diverse images, integrating physiological inputs, and studying advanced technologies such as infrared photography to increase the system's reliability and performance across a wide range of real-world conditions.  
Conceptualization, T.S.D. and M.S.; methodology, S.M. and A.K.; validation, M.H.R., D.P., and Y.L.; formal analysis, M.A.S.S.; investigation, M.S. and T.S.D.; resources, S.M. and J.Y.R.; data curation, M.S. and T.S.D.; writing--original draft preparation, A.K., T.S.D., and S.M.; writing--review and editing, A.K., M.A.S.S. and J.Y.R.; supervision, M.S., S.M., and J.Y.R.; funding acquisition, J.Y.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the faculty research fund of Sejong University in 2024.  
Not applicable.  
The data utilized in this research paper were collected from two publicly accessible sources: Kaggle ([[https://www.kaggle.com/datasets/](https://www.kaggle.com/datasets/)]([https://www.kaggle.com/datasets/](https://www.kaggle.com/datasets/)) (accessed on 19 January 2025)) and Health Informatics ([[http://www.healthinformaticslab.org/supp/resources.php](http://www.healthinformaticslab.org/supp/resources.php)]([http://www.healthinformaticslab.org/supp/resources.php](http://www.healthinformaticslab.org/supp/resources.php)) (accessed on 19 January 2025)). We express our gratitude to both platforms for providing access to this valuable data, which significantly contributed to the depth and rigor of our analysis. The datasets are available for public use, allowing for further research and exploration in the field.  
We would like to extend our heartfelt thanks to the participants, [PERSON] and [PERSON], for their invaluable contributions to this research. We are particularly grateful for their consent to use their images for experimental purposes, which greatly enhanced the quality and impact of our study. Their willingness to participate was instrumental in advancing our research objectives. We are also thankful to the National Research Foundation (NRF) (2018R1D1A1B07043286), Korea, for sponsoring this research publication under Project BK21 FOUR (Smart Robot Convergence and Application Education Research Center.  
The authors declare no conflicts of interest.