The increasing demand for efficient and accurate attendance systems in educational institutions has driven the adoption of technology-based solutions. This paper presents a comprehensive review of FaceAttend, a face recognition-based system designed to enhance college attendance management. Traditional attendance systems, including manual and card-based approaches, are prone to inaccuracies, time inefficiencies, and fraudulent practices such as proxy attendance. FaceAttend leverages advancements in artificial intelligence, computer vision, and machine learning to address these challenges.The system employs facial recognition technology to authenticate student identities in real-time, ensuring accuracy and eliminating manual intervention. This review highlights the system’s architecture, including its integration of pre-trained models, image preprocessing techniques, and cloud-based storage for scalability. Additionally, the study evaluates the system's performance based on factors such as recognition accuracy, speed, user acceptance, and security against spoofing.Results from case studies conducted across various educational institutions demonstrate the effectiveness of FaceAttend in streamlining attendance management, reducing administrative overhead, and fostering a culture of accountability. Challenges such as privacy concerns, hardware requirements, and algorithmic bias are also discussed, along with proposed solutions to mitigate these issues. The paper concludes by outlining future directions for improving the system, including multi-factor authentication and integration with learning management platforms. FaceAttend emerges as a promising solution for modernizing attendance systems in the education.
Face Recognition, Deep Learning, Cutting-edge, Attendance logging, Robust performance
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