Attendance management is a fundamental administrative task in educational institutions and organizations; however, conventional methods such as manual roll calls, sign sheets, and card-based systems are time-consuming, error-prone, and vulnerable to proxy attendance. To overcome these limitations, this research paper presents the design and implementation of a computer vision–based face recognition attendance system that automatically identifies individuals and records attendance in real time using facial features. The proposed system integrates image processing and deep learning techniques to detect, recognize, and verify human faces from live video streams captured through a camera. Initially, face detection is performed to localize facial regions from input frames, followed by preprocessing steps such as normalization, alignment, and noise reduction to improve recognition accuracy. A convolutional neural network (CNN)-based face recognition model is employed to extract discriminative facial embeddings, which are then compared with a pre-trained facial database using similarity metrics. Upon successful recognition, the system automatically logs attendance along with date, time, and confidence score into a centralized database. The system is designed to operate in real-world environments and is robust to variations in illumination, facial expressions, pose, and minor occlusions. Experimental results demonstrate that the proposed approach significantly improves accuracy and efficiency compared to traditional attendance systems, while minimizing human intervention. The automation of attendance tracking not only saves time but also enhances reliability, security, and scalability. This research highlights the potential of computer vision and deep learning technologies in developing intelligent, contactless, and efficient attendance management solutions suitable for modern smart environments.
Artificial Intelligence, Information Retrieval, Natural Language Processing, Machine Learning, Algorithms
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