Paper Contents
Abstract
With increasing demand for automation and reliability in organizational management, AI-driven attendance systems have become essential across education, corporate, and government sectors. This paper presents the design and implementation of an AI-powered attendance system that leverages facial recognition and intelligent verification for accurate and efficient tracking. The system integrates deep learningbased face detection (Haar CascadeCNN), feature extraction with convolutional neural networks, and real-time database synchronization for seamless record management. Key challenges addressed include identity spoofing, varying lighting conditions, and scalability across large user bases. Our modular architecture ensures cross-platform deployment and integration with existing human resource or academic management systems. Experimental results demonstrate an accuracy rate above 95% with average processing time under two seconds per recognition cycle. Limitations such as performance in extreme crowd density and partial occlusions are discussed, along with strategies for optimization. This work highlights a practical, secure, and extensible solution for intelligent attendance management.
Copyright
Copyright © 2025 Yogesh Chandrabhan Shinde. This is an open access article distributed under the Creative Commons Attribution License.
