Acta Scientific Computer Sciences

Research Article Volume 4 Issue 3

Driver Drowsiness Detection

Hemang Thakur1, Deval Arora1, Ramkiran Sampathi1, Shree Rukmini Thumu1 and Parisa Naraei2*

1Department of Artificial Intelligence and Machine Learning Lambton College Toronto, Canada
2Cestar College of Business, Health and Technology Research and Innovation Center, Lambton College Toronto, Canada

*Corresponding Author: Parisa Naraei, Cestar College of Business, Health and Technology Research and Innovation Center, Lambton College Toronto, Canada.

Received: November 27, 2021; Published: February 24, 2022

Abstract

CNN models are widely used to implement business solutions in the computer vision domain. In this paper, we have built a CNN based face detection model along with a facial feature detection using dlib library as accurate and light weight available libraries. This allows these models to run efficiently even on low-power devices. As a proof of concept, the paper presents a desktop application of the effective solution in the real-world scenario. In this approach, the images are captured at an interval of 0.1 seconds as the models take roughly 50 to 80 milliseconds to predict the output for a single image. As a result, the model is able to detect the driver drowsiness with an accuracy of 92.5% taking into account the state of mouth as well as eyes.


Keywords: Face Detection; Drowsy Driving; Convolution Neural Networks; Real Time Image Analysis

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Citation

Citation: Parisa Naraei., et al. “Driver Drowsiness Detection". Acta Scientific Computer Sciences 4.3 (2022): 08-13.

Copyright

Copyright: © 2022 Parisa Naraei., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

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