Smart Attendence Recording System Utilizing CNN and Edge Computing Techniques

Technological advancement has created multiple chances to develop innovative solutions for attendance management systems in different sectors. The paper introduces an edge-computing and machine learning-based attendance tracking system that offers immediate and dependable management through real-time operations. The system implements CNN facial recognition technology, which enables precise verification of individuals regardless of lighting conditions or how their face appears and whether their face is partly obstructed. Maximizing the advantages of speed and bandwidth together with data privacy demands the processing of data right at the edge devices. The system functions at affordable rates with energy efficiency while keeping cost-effectiveness which makes it suitable for houses and events alongside schools. The system succeeds in tests by delivering satisfying results while maintaining complete privacy protection and system security. This paper examines how merging deep learning technology with edge computing functions can upgrade current attendance systems into automatic systems suitable for organizations updated requirements.