Year: 2025
Type: article
Abstract: Efficient workforce management is crucial in in- dustrial environments where precise monitoring of employee attendance directly impacts productivity and operational ef- ficiency. This paper presents the design and implementation of an advanced attendance management system tailored for industrial applications, which integrates biometric authentication and facial recognition technologies. The system leverages real- time data processing capabilities to ensure accurate and auto- mated tracking of employee attendance, eliminating traditional manual errors and enhancing security. The proposed solution utilizes deep learning algorithms for facial recognition, coupled with biometric verification, to provide a robust, scalable, and efficient attendance tracking mechanism. The system also ensures seamless data integration with enterprise resource planning (ERP) solutions, thus enhancing decision-making and workforce analytics. This study explores the architectural framework, im- plementation methodologies, and performance evaluation of the system in an industrial setting. The experimental results indicate a significant improvement in accuracy and efficiency compared to conventional attendance management approaches. Index Terms—Attendance management, facial recognition, biometric authentication, real-time data processing, industrial workforce tracking, deep learning, ERP integration.
DOI: 10.55041/ijsrem43628
Authors: Samiksha Jagtap, Amey Kadam, Om Kadam
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409117502