Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 7 — Issue 2 — June 2025

Intelligent Drowsiness Detection using Haar Cascade Classifier and Convolutional Neural Network

https://doi.org/10.36548/jucct.2025.2.006
Ashokkumar Janarthanan
University College of Engineering, Villupuram
Arjun Paramarthalingam
University College of Engineering, Villupuram
Sindhuja Sundaresan
Surya Group of Institutions, Villupuram
Yokeshvaran S.
University College of Engineering, Villupuram
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Janarthanan, Ashokkumar, Arjun Paramarthalingam, Sindhuja Sundaresan, and Yokeshvaran S. 2025. “Intelligent Drowsiness Detection Using Haar Cascade Classifier and Convolutional Neural Network”. Journal of Ubiquitous Computing and Communication Technologies 7 (2): 176-94. https://doi.org/10.36548/jucct.2025.2.006.
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Keywords

— Drowsiness detection
— HAAR Cascade Algorithm
— Convolutional Neural Network (CNN)
— Eye tracking
— Facial recognition
— Driver monitoring system
— Deep Learning
Published: 15-07-2025

Abstract

Advanced driver alert systems are enabled by computer vision technology. They warn drivers of drowsiness and fatigue in a bid to save lives from horrific accidents. Drivers should stay alert, read traffic signs, and drive defensively, as driver sleepiness is a huge threat. The drowsiness detection support systems are now a necessity in the automotive industry. By keeping drivers' eyes on the road, they assist in highway safety and reduce deaths. Their usage reduces drowsy driving by a significant percentage and provides all people with safer roads to travel on. Eye monitoring has revolutionized driver safety. Haar-Cascade-based computer vision sensors and CNN algorithm-based computer vision sensors track facial landmarks in real-time. These systems calculate eye-to-eye ratios to infer signs of sleep, providing critical information about driver status, alertness, and fatigue. The emerging technology translates raw data into usable intelligence, with road safety taking precedence over exact face recognition and neural network processing. Face recognition identifies the eyes, whether left or right, and determines eye state open or closed based on intensity values and the space between eyebrows and eyelashes. Threshold crossing establishes open eyes; below the threshold indicates closed eyes. A sequence of closed-eye frames raises an alarm. The algorithm proves to be 90% effective on varied faces. Low computational requirements and real-time processing make it an ideal application for surveillance. The accuracy and efficiency of the system make it an investment that should be considered for surveillance activities.

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