Drowsiness Detection Using Eye Blink and Facial Features Image Analysis

Authors

  • Ajay S.1, Azariah John K.1 R. Subhashini2, Joshua Thomas3

DOI:

https://doi.org/10.37506/mlu.v20i4.1756

Keywords:

EAR (Eye Aspect Ratio), Fatigue, Drowsiness, Blink Detection, SMS alert.

Abstract

Drowsiness detection and alert system is developed using OpenCV library and deep learning algorithms and
implemented with a night vision camera and a computer to detect if a person is drowsy. The system uses a
camera to capture the person’s face and eyes to detect fatigue. In such a case where fatigue is detected, a
warning signal is issued to alert the person and an SMS to a person related to the victim

Author Biography

  • Ajay S.1, Azariah John K.1 R. Subhashini2, Joshua Thomas3

    1Student, 2Professor, Deartment of Information Technology, Sathyabama Institute of Science and Technology,
    Chennai, India, 3Senior Lecturer, Department of Computing, UOW Malaysia KDU Penang University College,
    Malaysia

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Published

2020-11-18

Issue

Vol. 20 No. 4 (2020): Medico-Legal Update

Section

Articles

How to Cite

Drowsiness Detection Using Eye Blink and Facial Features Image Analysis. (2020). Medico Legal Update, 20(4), 27-30. https://doi.org/10.37506/mlu.v20i4.1756