Real-Time Drowsiness Detection

@article{1712571,
      author = {Sufiya Begum, Safdar Iqbal, Mohammad Foorqan, Md Zahoor Rahi, Md Asif Alam},
      title = {Real-Time Drowsiness Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {140-145},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712571.pdf},
      abstract = {Driver drowsiness is a major cause of road acci- dents, especially during long-distance and night-time driving. Fatigue severely degrades reaction time, decision-making ability, and situational awareness, leading to life-threatening incidents. Existing detection techniques such as physiological sensing and vehicle behavior monitoring often suffer from intrusiveness or environmental sensitivity. This paper presents a real-time hybrid drowsiness detection system combining the speed of Haar Cascade classifiers with the accuracy of a Convolutional Neural Network (CNN) for open/closed eye-state classification. The system continuously monitors eye regions and evaluates prolonged eye closure using temporal thresholding to determine the onset of drowsiness. The lightweight design ensures real-time performance at 24?28 FPS on standard CPU hardware, achieving an accuracy of 96%. The proposed system effectively addresses limitations in classical EAR-based systems identified in previous research, including issues with illumination, spectacles reflection, and head movement. Experimental results demonstrate strong robustness and make the approach suitable for integration into low-cost ADAS systems.},
      keywords = {Drowsiness Detection, CNN, Haar Cascade, Eye- State Classification, Computer Vision, Deep Learning.},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712571}
  }