Abstract
This research explores the implementation and advantages of utilizing facial recognition technology for automated student attendance management in educational environments. The study outlines a comprehensive system that employs a high-resolution digital camera to detect and recognize students' faces, comparing them with stored images in a database. Successful matches result in real-time attendance logging, enhancing record-keeping and future analysis. The system also accommodates the inclusion of new images when no match is found. By embracing deep learning advancements, the technology offers an efficient alternative to conventional roll-call procedures, enabling automatic head counting in classrooms. The system integrates various face recognition algorithms, leveraging attributes such as shape, color, Local Binary Pattern (LBP), wavelet transforms, and auto-correlation. While challenges like varying lighting conditions, facial expressions, and face orientation are acknowledged, this research underscores the potential of technology to revolutionize attendance management and student engagement in education. Key Words: facial recognition technology, student attendance, automated system, deep learning, real-time logging, attendance management, classroom headcount, face recognition algorithms, record-keeping.
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