Transfer Learning Approach to Enhance a Student Attendance System Through Fingerprint Recognition
Slimane Ennajar, Walid Bouarifi · Atlantis highlights in social sciences, education and humanities/Atlantis Highlights in Social Sciences, Education and Humanities · 2025
This study investigates a new transfer learning approach designed to improve the accuracy and efficiency of a student attendance system using advanced fingerprint recognition methods.By employing pre-trained models such as the Xception and InceptionResNetV2 architectures, the research highlights the significant benefits of transfer learning in enhancing fingerprintbased student identification.The method strategically utilizes data augmentation techniques to address challenges related to limited dataset sizes, ensuring robust validation and improved scalability.The evaluation of the approach encompasses various performance metrics, including accuracy, precision, recall, F1 score, and MCC, providing a comprehensive understanding of its effectiveness.Achieving a 100% accuracy rate in fingerprint recognition underscores the reliability and practical feasibility of the transfer learning approach in student attendance systems.This study offers valuable insights into the intersection of biometrics and educational technology, facilitating the development of precise attendance tracking systems with broader applications in educational environments.The success of the proposed approach suggests promising opportunities for further research and development, indicating a positive trajectory for improving student-focused technological solutions.