Face Recognition for Attendance Management System

Abstract

In the era of modern technologies emerging at rapid pace there is no reason why a crucial event in educational sector such as attendance should be done in the old boring traditional way. Attendance monitoring system will save a lot of time and energy for the both parties students as well as the class teachers. Attendance will be monitored by the face recognition algorithm by recognizing only the face of the students from the rest of the objects and then marking them as present. The system will be pre feed with the images of all the students and with the help of this pre feed data the algorithm will detect them who are present and match the features with the already saved images of them present in the database

Introduction

Purpose

The system automates the student attendance process using facial recognition, reducing manual effort and saving time for both students and teachers. It captures student images and marks attendance automatically using face recognition algorithms.

Key Components

1. Authentication Methods Context

• Traditional methods: manual sign-ins, ID cards, fingerprint scanners.

• Modern shift: face recognition for non-intrusive, secure, and efficient identification.

2. System Installation Requirements

Software:

• OS: Windows

• Language: Python

Hardware:

• Processor: Intel Pentium 4 or equivalent

• Hard Disk: ≥ 40 GB

• RAM: ≥ 256 MB

3. Core Technology

• Uses Local Binary Pattern Histogram (LBPH) for face recognition.

• Reliable under varied conditions and suitable for identifying individuals from photo samples.

4. System Features

• Add/Edit/Delete student details.

• Capture and train on student photo samples.

• Import/export attendance as CSV files.

• Real-time face recognition-based attendance marking.

• Integrated chatbot for user-developer communication.

5. Modules Overview

1. Login System – User registration and secure login.

2. Student Management – Maintain and manage student records.

3. Photo Training – Collect and train face data for recognition.

4. Attendance Module – Auto-marks attendance via face detection.

5. Reports – Export data in CSV/Excel formats.

6. Developer Support – Contact support through chatbot.

7. Exit Module – Safe system exit without data loss.

6. Database

• Uses a Relational Database Management System (RDBMS) to store student and attendance data.

7. User Interface

Includes modules like:

• Login, Registration, Home, Student Details, Photo Capture, Face Detection, Attendance, and Chatbot.

8. Results & Performance

• Successfully captures and identifies student faces.

• Attendance marked and exported reliably.

• Challenges include:

  • Lighting variations

  • Partial occlusion of faces

Conclusion

Face recognition-based attendance systems offer a transformative solution for automated attendance management. Despite challenges like environmental variability and privacy concerns, advancements in machine learning and biometric technology continue to improve their reliability and acceptance. Future research should focus on enhancing robustness, ensuring data security, and exploring novel applications across various domains.

References

[1] Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. [2] Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823). [3] Liu, W., & Wechsler, H. (2002). Comparative Assessment of Independent Component Analysis (ICA) and Principal Component Analysis (PCA) for Face Recognition. IEEE Transactions on Neural Networks, 13(6), 1330-1335. [4] Ahonen, T., Hadid, A., & Pietikäinen, M. (2006). Face Recognition with Local Binary Patterns. In Proceedings of the 8th European Conference on Computer Vision (ECCV 2006). [5] Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face Recognition: A Literature Survey. ACM Computing Surveys (CSUR), 35(4), 399-458. [6] Bangalore University. (2025). Official Website. Retrieved from https://eng.bangaloreuniversity.ac.in/

Copyright

Copyright © 2025 Arun Kumar Tiwari, Sneha Rai, Asst. Pro. Rehan Ahmad. 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.