REAL-TIME DRIVER DROWSINESS DETECTION VIA EYE STATE RECOGNITION

Authors

  • Hajra Asif Author

Keywords:

Driver Drowsiness Detection, Road Safety, Driver Monitoring System, Smart Vehicle Systems, CNN, RNN, LSTM, Real-Time Monitoring, Deep Learning, Eye State Identification, Behavioral Analysis

Abstract

This paper presents a real-time driver drowsiness detection system based on eye state recognition, achieving 97% accuracy. The system combines CNN and RNN models to analyze live video feeds and issue timely alerts to prevent accidents caused by driver fatigue. The model is trained on a balanced dataset of 4,760 images, equally split between open and closed eyes, captured under varied driving conditions.

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Published

2024-12-31

Issue

Vol. 1 No. 1 (2024): International Journal of Engineering Research

Section

Articles