The rapid growth of Artificial Intelligence (AI) and machine learning has transformed automation in various fields, including education and corporate sectors. Traditional attendance systems that depend on manual entry or RFID cards are often time-consuming, inaccurate, and susceptible to proxy attendance. To address these issues, this paper proposes an AI-based smart attendance system that utilizes face recognition technology for real-time, contactless attendance marking. The system employs computer vision and deep learning models, particularly convolutional neural networks (CNN), to detect and recognize faces with high precision. Live camera input captures facial features, which are processed through image enhancement and feature extraction algorithms before being matched with a pre-trained dataset for identity verification. The system is scalable and capable of handling multiple users simultaneously. Experimental analysis shows that the model achieves over 95% accuracy under different lighting, facial poses, and occlusion conditions. This automated and secure approach reduces human intervention and offers a reliable, efficient, and intelligent alternative to traditional attendance methods.
Chaitanya et al. (Thu,) studied this question.