Real Time Eye Monitoring System Using CNN for Drowsiness and Attentiveness System

ABSTRACT

As computer science is evolving it can be used for a wide variety of things, for the scope of our research we look to track eye movements for the drowsiness and attentive detection of a person. The movements of eyes or feeling drowsiness can have many adverse effects on an individual, and even can lead to catastrophic situations. Eye Monitoring System for Drowsiness and Attentiveness Detection is developed for monitoring the drowsiness and attentiveness of any individual. A lot of drivers suffer from drowsiness while driving the cars, trucks and auto rickshaws. The reason for drowsiness can differ from person to person but feeling drowsy while driving can lead to various safety issues for the driver and other people on the road. A driver feels drowsiness during driving due to lack of sleep, tiredness and due to long travel driving. Drowsiness can also be felt by students in online classes and students tend not to be interested in online classes as there is no faculty keeping an eye on them since they are not in the classrooms physically. As covid-19 impacted education institutions the only way to fill the gap between the students and faculty is online classes, hence online classes are the only solution left. Not only in India but throughout the world online classes are used. A system for checking the attentiveness of students is an active area of research. Our aim is proposing a solution to detect the drowsiness of the driver while he is driving and inattentiveness of students in online class. The main purpose of this research paper is to detect drowsiness and attentiveness using different algorithms and techniques and use the one with best accuracy in our system. The techniques used are Euclidean and CNN. The paper also provides a comparison between these techniques. © 2021 IEEE.