Smart Attendance Monitoring with Facial Biometrics

This paper introduces an innovative attendance system utilizing facial recognition technology and incorporating a defaulter list. The proposed system employs advanced facial recognition algorithms to identify and log attendance for individuals. A detailed database is maintained, storing facial images of all registered individuals. When a person appears before the camera, the system identifies their face and marks their attendance automatically. The system also includes a defaulter list, highlighting those who were expected to be present but did not attend. This list is dynamically generated based on attendance records and serves to facilitate appropriate actions, such as sending notifications or reminders to the individuals. The facial recognition system relies on cutting-edge computer vision algorithms to extract facial features, including the distance between eyes, nose shape, and mouth shape, enabling accurate person identification The system accurately identifies individuals by matching the extracted facial features with those stored in its database. Its adaptability makes it suitable for use in a range of settings, such as schools, workplaces, and other environments that require consistent attendance monitoring. By automating the attendance process, the system reduces manual effort and enhances the accuracy of records. Additionally, the built-in defaulter list serves as a proactive measure to promote adherence to attendance policies.