Citation

Ahmad, Muhammad Shahrul Zaim and Ab. Aziz, Nor Azlina and Ghazali, Anith Khairunnisa (2024) Development of Automated Attendance System Using Pretrained Deep Learning Models. International Journal on Robotics, Automation and Sciences, 6 (1). pp. 6-12. ISSN 2682-860X

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Abstract

Abstract - Smart classroom enables better learning experience to the students and aid towards efficient campus' management. Many studies have shown positive correlation between attendance and student's performance, where the higher the attendance, the better the student's performance. Therefore, many higher learning institutions make class attendance compulsory and students' attendance are recorded. Technological solutions for an advanced attendance system such as face recognition is highly desirable. The authenticity of attendance can be ensured by using such solution. In this work, artificial intelligence based face recognition system is used for attendance recording system. The recognized face is used to confirm the presence of a student to the class. Six pretrained face recognition model are evaluated for the adoption in the system developed. The FaceNet, is adopted in this work with accuracy of more than 95%. The automation system is supported by IoT.

Item Type:	Article
Uncontrolled Keywords:	Attendance System, Face Recognition, FaceNet
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	04 Sep 2024 03:48
Last Modified:	04 Sep 2024 03:48
URII:	http://shdl.mmu.edu.my/id/eprint/12962

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