Real-Time Face Recognition System for Student Monitoring

Traditional classroom monitoring systems rely heavily on manual observation, making them prone to bias, inaccuracy, and inefficiency. Teachers in universities often lack long-term familiarity with students, leading to difficulties in evaluating their attentiveness, engagement, and overall behavior. To address these challenges, this project proposes an AI-powered, real-time face recognition system designed to identify, track, and analyze student behavior during lectures. The system leverages computer vision and deep learning models to detect and recognize faces in live video streams, classifying each student’s behavior as attentive or distracted based on facial cues and activity patterns. The collected data is stored in a structured MongoDB database and presented via intuitive web and mobile interfaces built using React.js and Flutter, respectively. This system aims to assist educators in gaining data-driven insights into classroom dynamics, enabling fairer evaluation and improved management of student engagement. Additionally, the model can integrate with existing attendance systems to automatically mark students as present if they remain attentive for a defined duration. By combining artificial intelligence and computer vision, this solution enhances transparency, promotes active learning, and reduces the administrative workload associated with monitoring student behavior.