Attendify: AI Based Smart Attendance System for Classroom
Abstract
This project provides a solution for managing student engagement and attendance within the classroom by implementing an attendance and engagement monitoring system to enhance classroom management using facial recognition technology for automating student attendance and measuring student attentiveness in real-time through the use of facial recognition. There is a total of three user types: Admin, Teacher, and Student. Each user type has its functionality within the system specific to their needs. Thereby, it provides a way to minimize manual processing, increase transparency, and provide useful data for institutions of higher education.
The Admin Module provides a web-based interface that allows administrators to manage users of the system through securely logging into the system, going to the dashboard and completing Create, Read, Update and Delete (CRUD) functions on teachers and students. Additionally, it allows administrators to view past detailed attendance records for each student in order to assist them with overseeing students and providing administrators with data needed to make correct, informed decisions.
The Teacher Module allows teachers to mark attendance by using a camera with real-world images, or by using a camera to take multiple pictures of students to mark as 'present.' Automatic face recognition is then used to mark the student present and also used to measure student engagement, thereby providing each teacher a detailed report of their students for each lecture.
The Student Module Allows for students to be able to log into the system in a secure manner and to view their attendance records based on daily or monthly record of their academic engagement with the institution. This creates transparency into student participation in their academic programs and thus encourages students to evaluate themselves in their participation of their academic program.
By combining both automated attendance tracking and attentiveness level Monitoring, this system provides educational institutions with a flexible and well organize solution for today's classrooms. It minimizes the challenges Introduce by manual attendance system and support better teaching and learning results
References
S. M. S. Islam, M. M. Rahman, and M. A. Hossain, ” Design and Implementation of a Smart Attendance System Using Facial Recognition,” IEEE Access, vol. 8, pp. 123456-123465, 2020
A. K. Gupta, R. S. Kumar, and P. Sharma,” Student Engagement Monitoring Using Facial Expression Recognition,” IEEE Transactions on Education, vol. 63, no. 4, pp. 234-242, Nov. 2020
J. Lee, H. Kim, and S. Park, ”Development of an Intelligent Attendance System Based on Facial Recognition,” IEEE Transactions on Industrial Informatics, vol. 15, no. 2, pp. 123-132, Feb. 2019.
R. Sharma, A. Kumar, and S. R. Soni, ”Student Engagement Detection Using Facial Expression Analysis,” IEEE International Conference on Artificial Intelligence and Education (ICAIE), Beijing, China, Jul. 2021, pp. 456-463.
L. Zhang, Y. Wang, and Z. Liu, ”Automated Attendance System Using Facial Recognition and Deep Learning,” IEEE Access, vol. 9, pp. 123456- 123465, 2021
K. S. R. Anjaneyulu, P. V. S. S. R. Anjaneyulu, and M. S. R. Anjaneyulu ,”Real Time Student Engagement Monitoring Using Facial Recognition,” IEEE International Conference on Signal Processing and Communications (SPCOM), Bangalore, India, Dec. 2020, pp. 234-240.
M. R. Islam, S. M. S. Islam, and M. A. Hossain, ”A Web-Based Attendance Management System Using Face Recognition,” IEEE International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, Dec. 2019, pp. 123-129
Refbacks
- There are currently no refbacks.