AI-Powered Real-Time Face Recognition Attendance System for Organizations

Abstract

This project focuses on developing a Face Recognition Attendance System using real-time image capture combined with machine learning and computer vision methods. The system streamlines the attendance marking process by using a CCTV camera to capture and recognize faces., eliminating the need for manual entry or biometric hardware. It leverages deep learning- based face recognition models for precise identification individuals and log their attendance in a connected database. The solution is designed to handle real-time inputs, manage user registration with live image capture, and ensure that each individual is marked only once per session. By automating the attendance process, this project improves operational efficiency, reduces time consumption, and minimizes human error. The system provides a scalable, accurate, and user-friendly alternative for institutions and organizations seeking a modern attendance solution.

Introduction

The text presents a modern attendance system leveraging real-time face recognition to automate tracking in educational institutions, offices, and secure environments. Traditional attendance methods—manual registers, RFID cards, or fingerprint scanners—are time-consuming, error-prone, and susceptible to proxy attendance. In contrast, AI-powered facial recognition ensures contactless, accurate, and efficient attendance tracking.

Key Features and Scope:

• Captures live images via webcams or CCTV cameras.

• Detects and identifies faces in real time using deep learning models (e.g., FaceNet, DeepFace).

• Matches faces against a database to automatically mark attendance.

• Prevents duplicate entries and supports offline functionality with later syncing.

• Allows automatic registration of new users and provides a user-friendly dashboard.

• Sends real-time notifications to individuals upon successful attendance marking.

Literature Insights:

• Multiple studies demonstrate high accuracy (82–96%) in real-time attendance systems using CNNs, MTCNN, FaceNet, Eigenfaces, and PCA.

• Systems show robustness under varying lighting, occlusions, and camera angles.

• Integration with CCTV and web applications enables scalability and reliability in classrooms and workplaces.

System Design and Workflow:

1. User Registration: Capture 50 facial images per employee; generate averaged facial embeddings stored in a secure database.

2. Face Recognition: Live video frames are processed to detect and identify faces; embeddings are compared using cosine similarity.

3. Attendance Logging: Attendance is marked automatically with timestamps for morning and afternoon sessions, preventing duplicates.

4. Notifications & Dashboard: Employees receive real-time alerts; administrators can monitor and export attendance records via a dashboard.

Methodology:

• Phases include data acquisition, preprocessing, feature extraction, identity verification, attendance recording, and notifications.

• Ensures secure storage, accurate recognition, and system scalability.

Tools and Technologies:

• Python & Flask: Backend web framework.

• MySQL: Database management.

• OpenCV & Deep Learning Models: Real-time face detection and recognition.

• Provides a lightweight, flexible, and scalable platform for deployment.

Conclusion

Facial detection tech keeps shifting the way we track attendance in schools and offices. It moves fast in that area. Old manual methods, such as calling out names or passing around sign-in sheets, eat up a lot of time. They open the door to mistakes too. Attendance is sometimes marked on behalf of individuals who are absent. Researchers stepped up to tackle those issues. They turned to automated options powered by AI. These setups run quicker and hit higher accuracy rates. They resist tampering in ways the old systems never could. This report looks at eight different papers on facial recognition for attendance tracking. Each one shows unique ways to build such systems. The work draws from computer vision and machine learning tools. It includes YOLO for picking out faces in the frame. CNNs handle the breakdown of facial details. LBPH and PCA come in for matching identities. A few systems pull from live video streams as things happen. Others capture still images at set intervals through a session. The software choices spread out widely. Python paired with OpenCV shows up often. Cloud services like the Azure Face API fill other roles. The common thread in every paper centers on easing the load of hands-on tasks. Reliability gets a big push forward. Adaptability to various settings stands out as key. Certain projects add mobile options for checking in. Real-time notifications keep everyone updated. Ties to current school platforms make integration smoother. Diving into these diverse methods reveals the rising role of facial recognition. It automates daily chores like attendance checks. The result goes beyond simple speed. Security strengthens across the board. The whole setup prepares better for what comes next.

References

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Copyright

Copyright © 2025 Mrs. Radhika S N, Siddarth K G , Sudharshan Swamy M, Taruna B V, Tejas H S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.