DESIGN AND IMPLEMENTATION OF AN AUTOMATIC ATTENDANCE SYSTEM USING FACIAL RECOGNITION
Abstract
Attendance monitoring is a fundamental activity in educational institutions and organizations for tracking presence and performance. Traditionally, this process is carried out manually, using paper-based registers or sign-in sheets. While simple, these methods are time-consuming, prone to human error, and susceptible to manipulation, such as proxy attendance. Manual and semi-automated attendance systems are inefficient and error-prone, often leading to inaccuracies in record-keeping and loss of instructional time. Existing biometric systems (such as fingerprint scanners) require physical contact, which may not be hygienic or feasible in a post-COVID-19 context. The aim of this study is to design and implement an intelligent, contactless, and automated attendance system using facial recognition technology. This study is significant in several respects such as it will offer schools and universities a faster, more reliable way to manage attendance, reducing administrative workload and improving record accuracy. The system was developed with OpenCV and convolutional Neural Network and it perform excellently well.
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