Abstract:
Attendance tracking systems are crucial for organizations to monitor the presence of
individuals in various settings such as workplaces, educational institutions, and events.
Traditional methods of attendance recording often suffer from inaccuracies, inefficiencies,
and susceptibility to manipulation. In response to these challenges, this study proposes the
development of an innovative attendance system using face recognition technology
powered by machine learning algorithms. This research aims to design and implement a
robust attendance system capable of accurately identifying individuals based on facial
features captured by a camera. The system leverages state-of-the-art machine learning
techniques for facial recognition, including deep learning models such as Convolutional
Neural Networks (CNNs). A comprehensive methodology is employed, encompassing data
collection, preprocessing, model training, and system integration. Experimental results
demonstrate the effectiveness of the proposed attendance system in accurately recognizing
individuals and recording their attendance. The system achieves high levels of accuracy, reliability,
and efficiency, thereby addressing the limitations of traditional attendance tracking methods.
Furthermore, the system's performance is evaluated under various real-world conditions to assess
its robustness and practical utility The advent of advanced technologies, particularly in the field of
machine learning and computer vision, presents an opportunity to develop more sophisticated and
reliable attendance tracking systems. One such promising technology is facial recognition, which
offers a non-intrusive and highly accurate means of identifying individuals. By leveraging facial
recognition technology, organizations can streamline the attendance process, reduce fraud, and
enhance overall efficiency. Despite these advancements, there are still challenges associated with
deploying facial recognition systems in real-world scenarios. These challenges include variations
in lighting conditions, facial expressions, occlusions, and the presence of similar-looking
individuals. Addressing these challenges requires robust data preprocessing techniques and the use
of advanced machine learning algorithms that can generalize well across diverse conditions.
