DRIVER DROWSINESS DETECTION SYSTEM BY REAL TIME EYE STATE IDENTIFICATION

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.