Driver Drowsiness Detection using Face Monitoring and Pressure Measurement

Authors

  • Abu Sayeed Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
  • Saad Ahmed Sadi Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

Keywords:

Fatigue detection, drowsiness, face detection, eye detection, pressure sensors, viola-jones algorithm

Abstract

Fatigue driving is the driver, after long periods of continuous driving, experiences mental and physical functional disorder. The international statistics shows that it is one of the major causes of accidents in the world. Detecting the drowsiness of the driver can issue timely warning that could help in preventing many accidents. In this research a system is designed to detect the drowsiness of a driver through face monitoring techniques and pressure measurement. The proposed approach is divided into two steps: detecting driver’s physiological performance and measuring the driver’s state. Physiological performance can be measured through pressure measurement using a force sensitive resistor. The second step is a computer vision method done by image processing. The computer vision method captures images and detects yawn and eye state. The results collected from the two of the above steps are merged with a drowsiness evaluation technique to detect drowsiness. The algorithm is proposed, implemented and tested and the experimental results provide a promising drowsiness indicator to prevent road accidents.

Cite this Article
Abu Sayeed, Saad Ahmed Sadi. Driver Drowsiness Detection using Face Monitoring and Pressure Measurement. Research & Reviews: A Journal of Embedded System & Applications. 2017; 5(3): 12–18p.

Author Biography

  • Abu Sayeed, Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
    Lecturer, dept. of cse, ruet,bangladesh

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Published

2018-03-06

Issue

Vol. 5 No. 3 (2017)

Section

Articles

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