Author(s) :

NIKHIL KUMAR.

Volume/Issue :

Abstract :

Traditional student attendance methods, such as roll calls and sign-in sheets, are time-consuming, error-prone, and vulnerable to proxy attendance. To address these issues, this study proposes an Automated Attendance Management System (AAMS) that integrates CCTV cameras, facial recognition, and GPS verification. The system captures students' facial images in real-time using classroom CCTV cameras without manual intervention, ensuring seamless and continuous attendance monitoring. Face detection is performed using a Haar Cascaded Classifier, and recognition is achieved through the FaceNet model, which compares captured embeddings with a pre-registered student database. Additionally, GPS-based location verification confirms that students are within the classroom’s geofenced area, preventing fraudulent attendance. This dual-verification system offers a secure, accurate, and efficient alternative to traditional attendance methods.

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