Face Recognition based Smart Attendance System using Cloud Computing

This research presents the development and implementation of a Face Recognition-based Attendance Collection system designed to automate attendance processes in institutional settings. Utilizing OpenCV for video capturing, Python for programming, and Azure Cloud services for backend processing, this system processes images via HTTP requests. The face recognition component employs Dlib’s ResNet model. The methodology outlines the system architecture, while experimental results are evaluated based on the number of faces accurately detected and recorded. The study also addresses the system’s limitations and proposes future enhancements. Aimed at streamlining attendance for teachers by leveraging existing room cameras, this system offers added benefits of security and ease of use, aiming to improve traditional attendance methods.