Improving Student Attendance using a Smart Biometric System with Facial Recognition using Insight Face and Cosine Similarity Algorithm

Student attendance is a critical factor influencing academic performance and success in higher education institutions. However, the rising trend of absenteeism among university students poses a significant challenge to academic integrity and administrative efficiency. In response to this issue, the present study proposes a smart classroom attendance system that leverages facial recognition technology as the primary biometric modality. The system incorporates a high-definition (HD) camera for real-time image acquisition, a deep learning-based facial detection algorithm, and a local server for data processing and storage. Upon successful facial verification, attendance is automatically recorded, eliminating the need for traditional manual sign-ins. The system is engineered to function reliably under varying classroom lighting conditions and offers seamless integration with institutional databases for efficient record management. To enhance recognition performance, various optimization techniques were applied, including parameter tuning and adjustment of the similarity threshold. The cosine similarity algorithm was employed to effectively match facial embeddings, contributing to high recognition accuracy. Additionally, the system features a user-friendly interface that enables educators to monitor student attendance, annotate records, and generate detailed reports with ease. Experimental results confirm the system’s effectiveness in minimizing attendance fraud and ultimately streamlining administrative tasks in higher education environments.