ATTENDANCE MONITORING SYSTEM
Ambika Gupta, Vaibhav Taank, Atish Kumar, Aman Aggrawal
School of Computer Science and Engineering
Galgotias University
Abstract - The Attendance Monitoring System using Face Recognition is a college project aimed at improving and automating the attendance management process. Traditional attendance systems involving manual paper-based methods or biometric fingerprint recognition have limitations in terms of accuracy, security, and efficiency. This project proposes a novel approach that utilizes face recognition technology to overcome these limitations.
The system leverages the advancements in computer vision and machine learning algorithms to accurately identify and authenticate individuals based on their facial features. It eliminates the need for physical contact or manual input, allowing for a seamless and contactless attendance tracking experience. By capturing facial images through a camera, the system compares the captured image with the pre-stored images in the database, and upon a successful match, records the attendance of the respective individual.
The project also incorporates real-time monitoring capabilities, allowing for immediate updates on attendance status. In addition, it provides comprehensive attendance reports and analytics, enabling efficient monitoring and analysis of attendance patterns. This facilitates proactive measures in addressing attendance-related issues and promotes accountability among students and faculty members.
The Attendance Monitoring System using Face Recognition not only simplifies the attendance management process but also enhances security and reduces administrative overhead. By automating the attendance tracking process, it saves valuable time and resources for educational institutions. Furthermore, it ensures the integrity of attendance data, minimizing the potential for errors or fraudulent practices.
The implementation of this system involves the integration of hardware components, such as cameras and computers, along with software modules responsible for image processing, face recognition, and database management. The project utilizes popular technologies and frameworks in computer vision and machine learning, such as OpenCV and deep learning algorithms, to achieve accurate and efficient face recognition.
