An Efficient Online Attendance Monitoring System Using FaceNet-Based FacialRecognition Technology

The needs created by the growth of online education and remote events have stimulated the requirements for efficient management and secure attendance administration systems. This paper describes an AI-based attendance solution that works by FaceNet recognition technology, aimed at ensuring attendance keeping and monitoring engagement among virtual classrooms and online meetings. The system consists of Using Multitask Convolutional Neural Networks (MTCNN) to detect the face and FaceNet to recognize the face, giving very high accuracy for identification during different lighting and environmental conditions. A unique option is that the system can start automatically recording a 3-second video clip with the start of the session to capture facial data and attendance information from participants. Periodically, the system will validate the participants' identities by checking any frames taken during the session against those in the facial database; any failure to match will record that session as absent. Moreover, it tracks real-time engagement through the analysis of head pose, eye position, and screen focus and alerts the facilitators when disengagement occurs. It is designed to be able to integrate with virtual platforms such as Google Meet and WebEx and Microsoft Teams, in addition to offering cloud security for storage and real-time analytics and automated report generation, tackling issues such as proxy attendance and fake logins. Scalability, low cost, and adaptability are the three main reasons why it has become one of the most advanced AI solutions for attendance verification and participant engagement in online learning and corporate settings.