Abstract: This paper proposes a Real-Time Student Face Recognition Attendance System using Artificial Intelligence (AI) to automate the attendance process in educational institutions. The system leverages state-of-the-art deep learning techniques, such as Convolutional Neural Networks (CNNs), for efficient face detection and recognition. It integrates facial recognition models with a real-time video stream, enabling automatic identification of students as they enter the classroom. The system first detects faces using a robust face detection algorithm, followed by face recognition to match the detected face with stored student data. The attendance is marked instantly, providing an accurate and seamless solution. The use of AI enhances the system’s performance, achieving high accuracy even in varied lighting conditions and with minimal facial occlusion. The system also incorporates security features, ensuring that it is resistant to impersonation or fraud. With easy integration into existing educational infrastructure, the proposed system offers a reliable and efficient alternative to traditional manual attendance methods, saving time, reducing human error, and ensuring transparency.

Keywords: Face Recognition Attendance System

| DOI: 10.17148/IARJSET.2025.125183