Design and Development of Real-Time Automated Facial Recognition Attendance Management System for Staff of FUTIA

• Author(s): Godfrey Okorafor Nwaji ; Whyte Asuquo Akpan ; Enefiok Usungurua
• Paper ID: 1710170
• Page: 605-612
• Published Date: 22-08-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 2 August-2025

Abstract

Attendance taken is one of the most important tasks that must be performed daily in every educational institution like universities, colleges, and schools, as well as organizations. In developing countries like Nigeria, majority of university attendance markings are done manually. The main goal of this project is to develop a real-time automated Facial Recognition-based attendance system that will eliminate the use of the manual management system. Face Recognition is a method of using various face recognition algorithms to identify or verify the identity of individuals via their face. This project utilized computer vision techniques to create face databases to load data into the recognition algorithm for implementation of this real-time automated attendance management system that accurately identified and track individual faces of university staff for attendance purposes. Some of the hardware and software tools utilize are USB camera, Personal Computer, OpenCV, developer kit, face detection and facial recognition algorithms, utilizing machine learning for recognition capabilities. This developed system that tracks attendance automatically is installed to reduce attendance administrative burdens, prevent proxy attendance, and enhance security at the school entrance gates. This system offers an efficient solution for attendance management, due to its enhanced functionalities, because it provides real-time notification, reports, and its capacity to integrate with existing infrastructure. The system is trained with university staff information, such as name, staff number, carder, and facial photographs. These facial images captured with camera are extracted using OpenCV, and the system compare the pictures taken and creates dataset using developer kit. Every moment recognition is concluded, the developed Excel sheet for staff attendance marking information is updated.

Keywords

Facial Recognition, Personal Computer, USB camera, Computer Vision, Face Databases.

Citations

IRE Journals:
Godfrey Okorafor Nwaji , Whyte Asuquo Akpan , Enefiok Usungurua "Design and Development of Real-Time Automated Facial Recognition Attendance Management System for Staff of FUTIA" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 605-612

IEEE:
Godfrey Okorafor Nwaji , Whyte Asuquo Akpan , Enefiok Usungurua "Design and Development of Real-Time Automated Facial Recognition Attendance Management System for Staff of FUTIA" Iconic Research And Engineering Journals, 9(2)