A COMPREHENSIVE SURVEY ON SMART ATTENDANCE SYSTEMS USING FACE DETECTION
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Abstract
Attendance monitoring is a fundamental administrative task in educational institutions and workplaces, where traditional manual methods are time-consuming and prone to errors and proxy attendance. Earlier automated approaches such as RFID and fingerprint systems reduce manual effort but require physical interaction and dedicated hardware. Recent advancements in artificial intelligence and computer vision have enabled contactless attendance systems based on face recognition.
This paper presents a comprehensive survey of face recognition-based smart attendance systems, analyzing various methodologies including classical techniques and modern deep learning approaches such as Convolutional Neural Networks (CNN), MTCNN, FaceNet, and other feature extraction models. The study compares their performance, advantages, and limitations in real-world scenarios.
The survey identifies key challenges in existing systems, including sensitivity to illumination variations, occlusion issues, lack of continuous presence monitoring, absence of exit detection, and high computational requirements. It highlights that most current systems focus only on identification rather than verifying actual presence duration.
Finally, the paper proposes future enhancements such as continuous face tracking, entry-exit detection, lightweight deep learning models, anti-spoofing techniques, and privacy-preserving architectures to improve reliability and scalability. These improvements aim to transform traditional attendance systems into intelligent, presence-aware monitoring solutions suitable for real-world deployment.
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