article · Journal of information and organizational sciences
A smart real-time attendance system has been developed using facial recognition technology to replace manual attendance methods in Nigerian universities. Traditional processes frequently suffer from proxy attendance, administrative burdens, and recording errors. To tackle this, the system combines Convolutional Neural Networks with the ArcFace algorithm for feature extraction and verification, built using tools such as InsightFace, OpenCV, and Streamlit. Testing demonstrates 94 percent accuracy in face detection, 98 percent in face recognition, and 96 percent overall attendance prediction accuracy. The platform automates reporting and calculates individual attendance rates to enforce the National Universities Commission requirement of 75 percent attendance for examination eligibility. Ethical measures, including data encryption, access restrictions, informed consent, and cross-profile fairness, are incorporated to deliver a secure and scalable institutional monitoring tool.
Manual attendance recording in large classes is prone to impersonation and administrative delays. By combining accurate facial recognition with automated compliance tracking, universities can reliably enforce exam eligibility standards. The integration of data encryption and fairness measures also provides a framework for handling sensitive biometric student data responsibly while reducing routine staff workloads.
The technology provides an applied software solution ready for institutional deployment by universities and higher education administrators. Developed using Streamlit and OpenCV, the system is designed to automate attendance audits and institutional policy compliance. Given its validated performance figures, the tool appears applied and tested, presenting an opportunity for university IT departments or educational management software providers to integrate automated biometric verification into existing student record systems.
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This study proposes a Smart Real-Time Attendance System using face recognition technology to address challenges in traditional attendance systems in Nigerian universities. These challenges include proxy attendance, manual errors, and administrative inefficiencies. The system employs Convolutional Neural Networks (CNNs) and the ArcFace algorithm for facial feature extraction and identity verification. Key development tools included InsightFace, OpenCV, and Streamlit, with Visual Studio Code as the IDE. The system ensures high accuracy, with 94% face detection, 98% face recognition, and 96% overall attendance prediction accuracy. It automates essential tasks like attendance percentage calculation and report generation, ensuring compliance with the National Universities Commission (NUC) 75% attendance requirement for exam eligibility. Ethical compliance was a core design concern, including informed consent, data encryption, access control, and fairness across facial profiles. This system significantly reduces impersonation, administrative workload, and enhances operational efficiency, making it a scalable and secure solution for attendance management. Its deployment is recommended for improving academic monitoring and policy enforcement in Nigerian universities.
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DOI: 10.31341/jios.49.1.8
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