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A Machine Learning Based Approach to Driver Drowsiness Detection

  • Swapnil MisalEmail author
  • Binoy B. Nair
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 835)

Abstract

Drowsy driving is a major cause of road accidents around the globe. A driver fatigue detection system that can alert the drowsy driver in a timely manner will therefore be of great help in improving road safety. This paper provides a non-invasive, camera-based innovative technique for detection of driver drowsiness based on eye blinking and mouth movement. A camera is mounted on the car dashboard facing the driver. First, face, eye and mouth of the driver are extracted from the images captured by the camera. Next, features for eyes and mouth are extracted and a classifier based detection system identifies if the driver is fatigued. Results demonstrate that the proposed system can efficiently identify indications of drowsiness on the drivers face.

Keywords

Yawning Eye PERCLOS Fatigue 

Notes

Acknowledgement

The authors like to thank the persons who readily accepted to be part of performance evaluation of system proposed in this paper.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.SIERS Research Laboratory, Department of Electronics and Communication EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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