Paper Key : IRJ************138
Author: Sarthak Raut
Date Published: 05 Apr 2025
Abstract
This research explores the implementation of face recognition technology in employee attendance systems to enhance accuracy, efficiency, and security. Traditional attendance tracking methods, such as manual registers and biometric fingerprint scanners, often suffer from inefficiencies, errors, and security vulnerabilities. The study employs a convolutional neural network (CNN)-based face recognition system, integrated with an attendance management database, to automate the attendance process. The methodology involves image preprocessing, feature extraction, and classification to ensure high accuracy in facial identification. The system was tested on a dataset comprising diverse facial features and lighting conditions, demonstrating an improved recognition rate and reduced false acceptance and rejection rates. The results indicate that face recognition provides a reliable, contactless, and efficient alternative for attendance tracking, minimizing time theft and proxy attendance. This research concludes that implementing face recognition technology in attendance management enhances operational efficiency and security, making it a viable solution for organizations.
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