Paper Key : IRJ************237
Author: Deepika V,Deva Dharshini S,Kanisha P,Sneha A,Porchelvi N
Date Published: 02 May 2024
Abstract
Manual attendance tracking is a time-consuming, error-prone, and frequently inefficient procedure used in organizations and educational institutions. As a result of its ability to improve accuracy, strengthen security, and expedite the process, automatic attendance systems have become more popular. In order to automate the administration of attendance, this study presents a creative Automatic Attendance System that makes use of face recognition and convolutional neural networks (CNN). The suggested solution starts by using a network of security cameras or webcams to take pictures of people as they go into a school or place of employment. During preprocessing, CNN-powered deep learning techniques are used to identify and extract facial features from these photos. For identification, the derived face traits are then compared to a previously created database of registered people's facial data. This identification method ensures high accuracy in tracking attendance since it is impervious to changes in illumination, face expressions, and small position alterations.
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