Abstract: Management of attendance is a fundamental com ponent of classroom assessment. Traditionally, manual processes, such as roll calls or attendance sheets, which are time-consuming and susceptible to errors. This paper proposes a smart attendance system that uses facial recognition technology to improve and optimize attendance tracking in educational institutions. Using advanced techniques like convolutional neural networks which is an algorithm specifically for deep learning that uses layers to perform convolution, activation, pooling, and other processes. CNN is used for object recognition tasks, such as image classification, detection, and segmentation, the system captures student images using high-definition cameras and compares them with pre-recorded data to mark attendance accurately. The system au tomatically updates the attendance records in a central database for administrative use. This smart and real-time method reduces human intervention, minimizes time waste, and eliminates errors, offering a reliable scalable solution for attendance management. By integrating emerging technologies like computer vision, this approach not only improves the attendance process but also establishes a foundation for enhancing overall organizational efficiency.
Keywords: facial recognition, attendance system, automation, Convolutional Neural Network (CNN), computer vision.
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DOI:
10.17148/IJARCCE.2024.131207
