Farzana Bente Alam

Work place: Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, 1229, Bangladesh

E-mail: farzana.alam@aiub.edu


Research Interests: IoT, Machine Learning, Software Engineering


Farzana Bente Alam is currently working as a Lecturer in the Department of Computer Science under the Faculty of Science and Technology at American International University-Bangladesh (AIUB). She completed her bachelor’s in computer science and engineering from American International University-Bangladesh in 2005 and her master’s in computer science from The University of Montana, USA in 2011. Machine Learning, Software Engineering, IOT, and Blockchain are some of her areas of research interest.

Author Articles
A Real-time Light-weight Computer Vision Application for Driver’s Drowsiness Detection

By Saikat Baul Md. Ratan Rana Farzana Bente Alam

DOI: https://doi.org/10.5815/ijem.2024.02.02, Pub. Date: 8 Apr. 2024

The issue of drowsiness while operating a motor vehicle is an increasingly common occurrence that has been found to contribute significantly to a substantial number of fatal accidents annually. The urgency of the current situation necessitates implementing a solution to mitigate accidents and fatalities. The present study aims to investigate a less intricate and less expensive but remarkably efficient approach for detecting drowsiness in drivers, in contrast to the existing complex systems developed for this purpose. This paper focuses on developing a simple drowsy driver detection system utilizing the Python programming language and integrating the OpenCV and Dlib models. The shape detector provided by Dlib is employed to accurately determine the spatial coordinates of the facial landmarks within the given video input. This enables the detection of drowsiness by monitoring various factors such as the aspect ratios of the eyes, mouth, and the angle of head tilt. The performance evaluation of the system under consideration is conducted through the utilization of standardized public datasets and real-time video footage. When tested with dataset image inputs, the system showed exceptional recognition accuracy. The performance comparison is done to show the efficacy of the proposed approach. Traveling can be made safer and more effective by combining the proposed system with additional safety features and automation technology in cars.

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