AI-Based Face Recognition Attendance System for Real-Time Monitoring

This paper presents an intelligent and automated attendance management system based on face recognition using Artificial Intelligence and deep learning techniques. Traditional attendance systems such as manual registers, RFID cards, and fingerprint-based methods are often time-consuming, prone to errors, and vulnerable to proxy attendance. Additionally, contact-based systems raise hygiene concerns in modern environments. To address these limitations, the proposed system utilizes Convolutional Neural Networks (CNN) and FaceNet-based facial embeddings to accurately recognize individuals in real time. The system captures facial images through a webcam, processes them using computer vision algorithms, and compares extracted features with stored data in a secure database. Upon successful recognition, attendance is automatically recorded with a timestamp. The system architecture includes a user-friendly web interface developed using Flask, along with a MySQL database for efficient data management. Experimental results demonstrate high accuracy of approximately 97.8% with fast processing time, making it suitable for real-world applications. The proposed solution enhances security, reduces manual effort, and provides a scalable, contactless, and efficient attendance monitoring system for educational institutions and organizations.