Real-time Driver Drowsiness Detection Using Deep Neural Networks
Daniel HalimM.Sc. SALWA F. ABDEL-MAJID FATMA A. HANAFYYoussef LotfyMohanad A. DeifRania Elgohary
Abstract
This paper presents a driver drowsiness detection for accident prevention which is based on the curvature of the eye. Our attempt is to develop a deep learning model that can use the input from a camera in real time by extracting the eyes to detect the drowsiness of the drivers.This paper helps to resolve the problem of drowsiness detection with an accuracy of 96% for test and 99% for validation
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