Smart Attendance Management using a Self-Supervised Learning Approach

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised Learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries.