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Driver Drowsiness Detection System Using Multi-Factor Detection
Subject area: Science,Engineering and Technology · Area of research: Computer Vision
DOI: https://doi.org/10.64388/IREV9I10-1716171
Abstract
Driver drowsiness is a major cause of road accidents, resulting in serious injuries and fatalities. This paper presents a real-time, non-intrusive Driver Drowsiness Detection System using multi-factor detection based on computer vision techniques. The system combines Haar Cascade classifiers for fast face detection with Dlib’s CNN-based facial landmark extraction to monitor key indicators such as Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and head-pose estimation. To enhance reliability, multi-cue fusion and temporal smoothing are applied to analyze patterns across consecutive frames, reducing false positives. A combined drowsiness score is generated, and real-time alerts are provided through voice and beep notifications to ensure timely intervention. The proposed system achieves a balance between accuracy and computational efficiency, enabling deployment on standard hardware. It offers a scalable and practical solution for improving road safety and intelligent transportation systems.
Keywords
Driver Drowsiness Detection, Computer Vision, Multi-factor Detection, Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR)
References
[1] Suresh Kumar, Chitrangad Singh Tomar, “Driver Drowsiness Detection and Alert System Using Computer Vision”, 2025.
[2] Vinay Kalisetti et al., “Analysis of Driver Drowsiness Detection Methods”, 2023.
[3] Eryl Nanda Pratama, Wikky Fawwaz Al Maki, “Drowsiness Detection System for Masked Face Based on DNN and Haar Cascade”, 2022.
[4] (Anonymous), “Driver Drowsiness Detection in Python”, 2022.
[5] Thum Chia Chieh et al., “Development of vehicle driver drowsiness detection system using electrooculogram (EOG)”, 2005.
[6] Meena Siwach, Suman Mann, Deepa Gupta, “A Practical Implementation of Driver Drowsiness Detection Using Facial Landmarks”, 2022.
[7] Vidushi Singhal et al., “Drowsiness Detection and Alert System using DLib”, 2023.
[8] M.Omidyeganeh, A. Javadtalab, S. Shirmohammadi, “Intelligent driver drowsiness detection through fusion of yawning and eye closure”, 2011.
[9] Shruti Mohanty et al., “Design of Real-time Drowsiness Detection System using Dlib”, 2019.
[10] Vinay Paliwal et al., “Driver Drowsiness Detection System Using Computer Vision”, 2025.
How to cite this paper
@article{1716171,
author = {Dharshanaa Sree T, Gana Sri M S, Swetha M, Vishmitha T, C. Janani},
title = {Driver Drowsiness Detection System Using Multi-Factor Detection},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1207-1211},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716171.pdf},
abstract = {Driver drowsiness is a major cause of road accidents, resulting in serious injuries and fatalities. This paper presents a real-time, non-intrusive Driver Drowsiness Detection System using multi-factor detection based on computer vision techniques. The system combines Haar Cascade classifiers for fast face detection with Dlib’s CNN-based facial landmark extraction to monitor key indicators such as Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and head-pose estimation. To enhance reliability, multi-cue fusion and temporal smoothing are applied to analyze patterns across consecutive frames, reducing false positives. A combined drowsiness score is generated, and real-time alerts are provided through voice and beep notifications to ensure timely intervention. The proposed system achieves a balance between accuracy and computational efficiency, enabling deployment on standard hardware. It offers a scalable and practical solution for improving road safety and intelligent transportation systems.},
keywords = {Driver Drowsiness Detection, Computer Vision, Multi-factor Detection, Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR)},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716171}
}