Article
An automated student real time attendance using face recognition
This paper presents a Real-Time Student Attendance System based on Face Recognition using Artificial Intelligence (AI) to automate attendance management in educational institutions. The proposed system employs advanced deep learning techniques, particularly Convolutional Neural Networks (CNNs), to perform accurate face detection and recognition. It combines facial recognition algorithms with a live video stream, allowing the system to automatically identify students as they enter the classroom environment. Initially, the system detects faces using a reliable face detection method, after which the recognized facial features are compared with previously stored student records in the database. Once a match is confirmed, the student’s attendance is recorded automatically, providing a fast and efficient solution. The integration of AI significantly improves the system’s performance, enabling high recognition accuracy even under different lighting conditions or in situations where slight facial occlusion occurs. In addition, the system incorporates security mechanisms to prevent impersonation or fraudulent attendance marking. Designed to be easily integrated into existing institutional infrastructure, the proposed framework provides a dependable and effective alternative to conventional manual attendance systems. By automating the process, the system reduces time consumption, minimizes human errors, and improves overall transparency in attendance management.
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