Cloud-Enabled Fingerprint Attendance Management System Using Convolutional Neural Networks in Educational Institutions

Challenges with accuracy, efficiency, and safety concern traditional techniques of managing attendance in educational institutions. A fingerprint attendance management system can be accessed over the cloud and incorporates convolutional neural networks (CNNs) for improved biometric authentication. With cloud computing, the system can store and analyze data centrally, making attendance records and analyses easily accessible. Using CNNs improves the speed and accuracy of fingerprint matching, and the suggested system uses fingerprint recognition as a reliable biometric modality. The system can adapt to different fingerprint patterns and environmental circumstances thanks to the convolutional neural network layers trained on a vast collection of fingerprint photos. Students' and teachers' attendance may be reliably and safely verified in this way. The link with the cloud also allows for real-time data synchronization, meaning authorized people may obtain attendance information instantaneously. The system uses encryption to further guarantee the security and confidentiality of sensitive biometric data in the cloud.