Open AccessFatigue Detection For Online Classes Based on Adaboost
Author(s) -
Qingyu Mo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1827/1/012121
Subject(s) - adaboost , face detection , class (philosophy) , face (sociological concept) , set (abstract data type) , computer science , artificial intelligence , focus (optics) , process (computing) , alarm , computer vision , facial recognition system , pattern recognition (psychology) , engineering , support vector machine , aerospace engineering , social science , physics , optics , sociology , programming language , operating system
Online Classes has been widely popularized since corona virus doesn’t allow students to have face-to-face classes. However, due to comfortable environment that students have, students may get sleepy in class and cannot help with their fatigue. So students need a supervised environment to alarm them not to fall asleep. So in this article, a real-time method to detect whether the student in front of the camera is sleeping, based on the demo of students in online classes, is introduced. This article focus on detection of states of eyes of students. It uses Adaboost as the training method to detect the face of the student, uses Canny to process the picture with human face, and calculates the area of the exposed eyeball and the radius of the eye. Finally, PERCLOS is used as the criterion to determine whether the student is sleeping in class. The method in this article has high detection rate on faces and eyes, which is respectively 99.7% and 98.5% for image set that made by the author. It can be applied on online classes and shows good results in detecting the fatigue of people in practical.