AI Base Face Recognition Attedance System

Authors: Namrata patel, Om Pancholi, Vedant Patel, Amlesh Sharma, Jainish Kachiyaa

Abstract: The rapid adoption of artificial intelligence (AI) and computer vision has transformed traditional attendance management systems, which are often inefficient, error-prone, and vulnerable to proxy marking. This research presents an AI-Based Face Recognition Attendance System developed using the MERN stack (MongoDB, Express.js, React.js, and Node.js) integrated with deep learning frameworks such as OpenCV and TensorFlow. The proposed system automates attendance tracking by capturing live facial images through a webcam or mobile camera, processing them in real time, and matching them against a secure database of registered users. Upon successful recognition, attendance is recorded automatically, eliminating the need for manual registers or RFID-based systems. The architecture combines a React-based interactive frontend, a Node.js/Express.js backend for secure communication, and MongoDB for scalable data storage. AI- driven face recognition ensures reliable identification even under varying conditions, while additional features such as role- based access, analytics dashboards, and real-time notifications enhance usability. This work demonstrates how integrating AI with modern full-stack web technologies can deliver a secure, contactless, and efficient attendance solution for educational institutions, corporate environments, and workplaces. Future enhancements include mobile application support, advanced anti-spoofing measures, and improved recognition accuracy under challenging conditions.