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DRIVER DROWSINESS DETECTION SYSTEM BY REAL TIME EYE STATE IDENTIFICATION


Article Information

Title: DRIVER DROWSINESS DETECTION SYSTEM BY REAL TIME EYE STATE IDENTIFICATION

Authors: Hajra Asif, Dr. Ghulam Mustafa

Journal: Spectrum of Engineering Sciences

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31

Publisher: Sociology Educational Nexus Research Institute

Country: Pakistan

Year: 2025

Volume: 3

Issue: 8

Language: en

Keywords: Deep learningRNNLSTMCNNReal-time monitoringRoad SafetyDriver monitoring systemDriver Drowsiness DetectionSmart Vehicle SystemsEye State IdentificationBehavioral Analysis

Categories

Abstract

The paper proposes a new architecture which plies eye states of a live video feed and receives at 97 percent accuracy; followed by sending signals at the right time before instances of accidents occur and this is an immense problem in the globe since traffic accidents by fatigued drivers are a huge menace. It is a combined CNN and RNN based system. A comprehensive dataset of 4,760 images, comprising 2,380 closed-eye and 2,380 open-eye images captured under diverse driving conditions, is used to train the model.


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