Comprehensive Smart Attendance Management System Utilizing Advanced Multi-Facial Recognition Technology

Abstract

This study introduces an innovative method to optimize student attendance tracking using Convolutional Neural Networks (CNNs) and data augmentation. Applying diverse transformations to facial image datasets and utilizing a pre-trained VGG16 model through transfer learning improves the robustness and inclusivity of attendance systems. The methodology leverages the strengths of CNNs and transfer learning to effectively use learned image representations, ensuring accurate and reliable attendance tracking.

Evaluation metrics confirm the model's suitability, demonstrating its potential for seamless integration with existing systems or independent deployment. This automated attendance recording system offers an intuitive interface for educators and administrators, enhancing the efficiency and accuracy of student management processes. The system streamlines the administrative burden by reducing manual labour and minimizing errors, allowing educators to focus more on teaching and less on administrative tasks.

The project highlights the transformative impact of deep learning and computer vision technologies in educational settings, promising significant improvements in monitoring and managing student attendance. Through this integration, academic institutions can achieve more efficient and accurate student tracking, reflecting the broader potential of advanced technologies in modernizing and improving educational administrative functions.

Furthermore, transfer learning allows the model to recognize students effectively even with limited labelled data, improving overall system accuracy and efficiency. This study underscores the potential of leveraging deep learning and computer vision to solve practical problems in educational settings, paving the way for more innovative and efficient approaches to student management.