Abstract:
Attendance tracing systems are crucial for organization to monitor the presence of
individuals in various settings such as workplace, 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.
