# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 5 Conclusions  
This paper introduces static and adaptive frame threshold algorithms for detecting driver drowsiness. The static threshold method employs predefined eye closure ratio (ECR) and mouth aperture ratio (MAR) thresholds (0.15 and 0.4, respectively) to classify drowsy and non-drowsy states. The adaptive threshold approach dynamically adjusts the number of consecutive frames analyzed, improving accuracy during prolonged driving sessions. The proposed methods are cost-efficient, independent of training requirements, and capable of real-time application. Unlike machine learning-based systems, these algorithms rely solely on lightweight computations and existing camera technologies, making them suitable for integration into various devices. Future research will explore blockchain integration to enhance data privacy and secure communication across Internet of Things (IoTs) networks. This direction aligns with the need for scalable and privacy-sensitive applications in transportation safety systems.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Modality** & **Advantages** & **Limitations** \\ \hline Deep Learning (CNN) [38] & Facial features., eyes & High accuracy, detects subtle patterns & High computational cost, requires large datasets \\ \hline YOLO-Based Detection [39] & Face and eye detection & Real-time, robust in varied conditions & Limited by occlusions, false positives \\ \hline Physiological Method [40] & Heart rate, pupil size & High sensitivity to drowsy states & Requires specialized sensors, invasive \\ \hline Proposed Method & ECR, MAR & Cost-efficient, training-independent, real-time & May struggle with extreme occlusions or rare variations \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of proposed method with SOTA.  
**Author Contributions:** Conceptualization, E.E., Y.I. and F.L.; methodology, E.E. and Y.I.; software, E.E., F.A. and W.E.; validation, E.E., Y.I., F.A. and W.E.; formal analysis, E.E.; investigation, Y.I. and F.L.; resources, F.L.; data curation, Y.I.; writing--original draft preparation, E.E. and Y.I.; writing--review and editing, F.L. and F.A.; visualization, W.E.; supervision, Y.I. and F.L.; project administration, Y.I.; funding acquisition, Y.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Louisiana Transportation Research Center through LTRC Project #22-2 TIRE.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** All data used in this study are available in the references cited within this paper. No new data were created or analyzed in this study.  
**Acknowledgments:** The authors acknowledge the support of the Louisiana Transportation Research Center in supporting the work performed in this paper through LTRC Project #22-2 TIRE. The authors would also like to thank the support of Southern University and A&M College for the tremendous support provided to finalize this work.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of this manuscript; or in the decision to publish the results.