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Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and

Eduardo Quiles-Cucarella1, Julio Cano-Bernet1, Lucas Santos-Fernández1

  • 1Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain.

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|September 14, 2024
PubMed
Summary
This summary is machine-generated.

Fatigue causes 10-20% of road accidents. A new multidimensional drowsiness detection system using facial expressions, gaze, and head position proves more effective than single-index methods for safer driving.

Keywords:
Haar featuresartificial visionbiometric informationdriver drowsiness detectiondriver monitoringfacial expressionshistograms of oriented gradients

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Area of Science:

  • Road safety
  • Human-computer interaction
  • Biomedical engineering

Background:

  • Road accidents linked to driver fatigue are severe, necessitating advanced detection systems.
  • Current drowsiness detection methods vary, with driver monitoring technologies underexplored.
  • Existing systems may rely on steering or vehicle data, but driver-focused monitoring offers potential.

Purpose of the Study:

  • To evaluate a multidimensional drowsiness index using facial expressions, gaze, and head position.
  • To assess the feasibility of implementing this system in a low-cost electronic package.
  • To develop and compare algorithms for real-time driver drowsiness detection.

Main Methods:

  • Developed a drowsiness detection algorithm using facial features (blinking, yawning, eye-opening), gaze direction, and head position.
  • Compared Haar features and Histograms of Oriented Gradients (HOG) for facial recognition.
  • Implemented the system on a Raspberry Pi for low-cost prototyping.

Main Results:

  • The multidimensional index demonstrated superior performance in detecting driver drowsiness compared to single-index approaches.
  • Facial recognition algorithms based on Haar features and HOG were successfully implemented.
  • A functional, low-cost prototype for drowsiness detection was created.

Conclusions:

  • A multidimensional approach to driver drowsiness detection is more effective than single-parameter methods.
  • Low-cost hardware like Raspberry Pi is suitable for developing practical driver monitoring systems.
  • This technology has the potential to significantly enhance road safety by mitigating fatigue-related accidents.