Accurate and secure attendance tracking has become a critical requirement in modern smart campuses, where traditional methods fail to ensure reliability and transparency. Conventional student attendance systems based on manual roll calls or standalone RFID technologies are time-consuming, error-prone, and susceptible to proxy attendance. To address these challenges, this paper proposes a smart student attendance management system that integrates RFID technology with deep learning-based face recognition using Convolutional Neural Networks (CNN). In the proposed approach, RFID is used for initial student identification, while a CNN-based face recognition model verifies the student’s physical presence through real-time camera input, thereby eliminating fraudulent attendance. The deep learning model effectively extracts discriminative facial features and performs accurate classification under varying lighting and pose conditions. Attendance records are automatically stored in a centralized database and accessed through a web-based interface. Additionally, an automated notification module sends absence alerts to students and parents, enhancing transparency, accountability, and communication in smart education environments
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