Attendance system based on face recognition using lbph method

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.