Real-Time Smart Class Management and Surveillance System Using Computer Vision
Keywords:
Computer vision, Deep learning, Face detection, Face recognition, LBPH (Local Binary Patterns Histograms), YOLO (You Only Look Once)Abstract
Automated classroom attendance systems based on facial recognition offer a contactless, reliable, and time-efficient alternative to conventional manual attendance procedures. This work proposes a real-time smart classroom management system that integrates classical computer vision methods with advanced deep learning–based facial recognition models to improve identification accuracy and system robustness. The proposed framework is specifically designed to operate effectively under realistic classroom environments, addressing practical challenges such as variations in illumination, facial pose, occlusions, and high student density. The system performs automated face detection, feature extraction, and recognition to accurately identify enrolled students and record attendance without human intervention. By minimizing manual effort, the proposed approach significantly reduces administrative workload and limits the possibility of proxy attendance or human error. Furthermore, the architecture supports real-time processing and is scalable, making it suitable for deployment across multiple classrooms and institutional settings. The integration of intelligent analytics within the classroom environment also opens opportunities for future extensions, such as student engagement monitoring and performance analysis, contributing to the development of smart and digitally enabled educational ecosystems.
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