AI-POWERED FACE RECOGNITION SYSTEM FOR AUTOMATED ATTENDANCE MANAGEMENT AND MONITORING

Abstract

Attendance management plays a vital role in educational institutions, corporate organizations, and government sectors for monitoring the presence and participation of individuals. Conventional attendance systems, including manual registers, RFID cards, and biometric devices, often suffer from limitations such as human errors, time consumption, proxy attendance, and increased administrative workload. To address these challenges, this paper presents an AI-Powered Face Recognition System for Automated Attendance Management and Monitoring that utilizes artificial intelligence and computer vision technologies to automate the attendance recording process. The proposed system employs facial recognition algorithms to detect, identify, and verify individuals in real time through a webcam without requiring physical contact or manual intervention. Advanced image processing techniques are applied to enhance facial feature extraction and recognition accuracy under varying lighting conditions, facial expressions, and viewing angles. Once a registered individual is recognized, the system automatically records attendance along with the corresponding date and time in a centralized database. The integration of artificial intelligence enables fast and reliable recognition while significantly reducing the possibility of proxy attendance and fraudulent activities. The system also provides monitoring, attendance reporting, and data management functionalities through an intuitive interface. Experimental evaluation demonstrates high recognition accuracy, real-time performance, and efficient attendance tracking capabilities. The proposed solution offers a secure, scalable, and cost-effective attendance management framework suitable for schools, colleges, universities, offices, and other organizations seeking intelligent and automated monitoring systems. By leveraging AI-driven facial recognition technology, the system improves operational efficiency, transparency, and reliability in attendance management.