Face Recognition-Based Attendance System Using Group Photos

Abstract

ThisprojectintroducesaPythonFlask-based.Web application aimed at simplifying student attendance tracking. The system leverages image processing techniques toanalyzeuploadedgroupphotosandautomaticallyidentify students marked as present. It offers a secure admin login, allowingadministratorstoeasilyuploadattendanceimages. Additionally,attheendofeachmonth,theplatformprovides a one-click feature that uses the Yagmail library to send notification emails to students whose attendance drops below 50%, promoting timely awareness and action. By combining Flask’s web framework with automated image analysis, this solution delivers an intuitive and efficient tool for managing attendance, reducing manual workload, and supporting better student engagement

Introduction

1. Objective

This project develops a web-based attendance system for educational institutions using Python Flask, image processing, and face recognition. It enables administrators to:

• Upload group photos

• Automatically detect and recognize students

• Accurately record attendance

• Reduce manual effort and errors

2. Key Features

• Flask framework: Used to build a scalable, lightweight web application

• Image processing with OpenCV: Detects and identifies faces in uploaded images

• Secure login: Only authorized administrators can access and manage attendance

• Email alerts: Automatically sends monthly notifications via Yagmail to students with attendance below 50%

• Admin dashboard: For uploading images and viewing attendance reports

3. Methodology

Steps in development:

1. Requirement Analysis – Define user roles and system features

2. Design – Build a database schema and define Flask routes

3. Environment Setup – Install Python, Flask, OpenCV, database

4. Frontend – Create user-friendly interface (HTML, CSS, JavaScript)

5. Backend – Implement Flask routes, face recognition logic

6. Testing – Verify functionality and fix bugs

7. Deployment – Host on platforms like Heroku or AWS

8. Documentation & Maintenance – Guide future use and updates

4. Results

The system:

• Successfully identifies and logs multiple individuals from a single group photo

• Reduces the need for individual face capture or manual attendance marking

5. Advantages

• ? High Efficiency – Faster than individual recognition

• ? User-Friendly – No need for perfect positioning

• ???? Less Intrusive – No close-up face shots required

• ???? Scalable – Handles large groups at once

• ???? Cost-Effective – Minimal hardware required

• ???? Proactive Communication – Alerts low-attendance students via email

6. Applications

• Schools & Universities – Classroom and event attendance

• Corporate Offices – Track training and meeting attendance

• Events & Conferences – Verify participant presence

• Government Agencies – Monitor staff attendance

• Hospitals – Track healthcare worker shifts

7. Limitations

• Performance may drop with:

  • Poor lighting

  • Obstructed or turned faces

  • Low-resolution images

• Requires regular updates and testing to maintain accuracy

8. Literature Survey Highlights

The system builds upon prior work in:

• Raspberry Pi & OpenCV attendance systems

• PCA & Eigenface algorithms for face recognition

• Skin classification techniques for detection accuracy

• Emphasis on portability, automation, and real-time accuracy

Conclusion

Developing a webapplicationforattendance trackingusingPythonFlaskandimageprocessingoffersan effective and user-friendly solution for educational institutions. By allowing administrators to upload group photos of students and automatically log attendance, the systemstreamlines the process andreduces manualeffort. Asecurelogininterfaceensuresthatonly authorized users can access the platform. Additionally, by integrating the Yagmail library,thesystem enables administrators tosend automated email notifications to students whose attendance falls below 50% at the end of each month. This method not only saves time but also promotes accountability and transparency in attendance management. Overall, leveraging image processing combined with modern web technologies enhances the reliabilityandefficiencyofattendancesystems,meetingthe evolving needs of educational environments.

References

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Copyright

Copyright © 2025 Mahendra Mistari, Puja D. Saraf, Sagar More, Lakshya Chaudhari, Nikhil Mahale, Piyush Tirmal . 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.