In recent years, an increase in the demand for modern transportation necessitates a faster car-parc growth. Driver drowsiness has been generally recognized as a significant factor in the increasing number of road accidents. To determine the level of fatigue, OpenCV technology uses the movement of the driver’s eyes and the position of the driver’s head. A web camera mounted in the car can be used to take a photo of the driver using image capture. Because of the way a video is created by the camera, the measurement must be performed on either side of the video stream to obtain the edges for future operations The situation in which the video was recorded is determined by partitioning the video into individual frames. The video will then be divided into edges for further exploration. At this point, the region containing the driver’s image is recognized. A predefined count is set for each package’s face region. Face recognition implies that, with a slight improvement in the use of the PC, we consider the important facial characteristics. The eyes of the face should be identified and classified for further investigation. The eyes are the most important decision parameter for assessing the driver’s state.

Author (S) Details

K. Vinutha
BMS Institute of Technology and Management, Yelahanka, Bangalore, India.

N. Ashwini
BMS Institute of Technology and Management, Yelahanka, Bangalore, India.

Amrit Raj
BMS Institute of Technology and Management, Yelahanka, Bangalore, India.

Jayam Sukruth
BMS Institute of Technology and Management, Yelahanka, Bangalore, India.

M. Praneeth
BMS Institute of Technology and Management, Yelahanka, Bangalore, India.

Shubham Anand
BMS Institute of Technology and Management, Yelahanka, Bangalore, India.

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