An Improved Version of Student Attendance Management System based on RFID using Raspberry-pi

Abstract

Student attendance monitoring is a routine but time-consuming task in higher education institutions. Existing systems often rely on manual processes or limited automation using RFID, biometrics, or mobile devices. This paper presents an improved attendance management system leveraging both RFID and real-time facial recognition. Enhancements have been made to the RFID hardware module and a user-friendly web interface for faculty operations. The proposed system integrates Raspberry Pi 3 and OpenCV to implement a five-stage face recognition pipeline: detection, preprocessing, training, recognition, and attendance logging. The system uses a modified Viola-Jones Haar Cascade for face detection, LBP histograms for recognition, and combines SQLite with MySQL for database management. Attendance is automatically recorded, and alerts are sent to guardians and the department in case of absences. This approach reduces human error, enhances security, and improves administrative efficiency.

Introduction

Problem Statement

• Traditional, paper-based attendance in educational institutions is inefficient, time-consuming, and prone to manipulation (e.g., proxy attendance).

• RFID-based systems help, but RFID tags can be shared, compromising reliability.

???? Proposed Solution

A hybrid attendance system combining:

• RFID for quick identification

• Facial recognition for biometric verification

• Implemented on a Raspberry Pi using OpenCV, LBPH, and dual databases (SQLite & MySQL)

???? Literature Insights

• RFID improves efficiency but is vulnerable to tag sharing.

• Biometric methods (fingerprints, facial recognition) offer more security:

  • Facial recognition is non-intrusive and suited for real-time classroom use.

• LBPH (Local Binary Pattern Histogram) is preferred for its accuracy and lightweight processing.

• Previous studies lacked integration of security, portability, and real-time alerts — this system addresses all three.

?? System Overview

???? Hardware Components

• Raspberry Pi 3 (core processor)

• EM-18 RFID Reader

• Pi Camera (facial capture)

• LCD Display & Buzzer

• Power Supply

????? Software Stack

• Raspbian OS + Python

• OpenCV for image processing

• SQLite (local) & MySQL (centralized) databases

???? System Workflow

1. Face Detection – via Haar Cascade classifiers.

2. Preprocessing – grayscale conversion, resizing, and contrast enhancement.

3. Face Training – dataset built during student registration using LBPH algorithm.

4. Authentication – both RFID and face match required for attendance.

5. Logging & Alerts – attendance recorded; absentee notifications sent via email/SMS.

???? Results & Performance

Testing Environment:

• 4 weeks

• 50 undergraduate students

Efficiency Gains:

• Traditional method: ~7 minutes for 50 students

• Proposed system: <1 minute, saving ~80% of time

Security:

• Dual authentication reduced proxy attendance

• Alerts triggered on mismatched RFID and face data

???? User Experience

• Faculty appreciated live displays and easy registration.

• Students liked the contactless interface.

• Minor issues with masks/glasses were addressed via more training samples.

?? Limitations

• Reduced accuracy in poor lighting or when faces are partially occluded

• Limited real-time speed (~2–3 FPS) due to Raspberry Pi constraints

• Requires network connectivity for syncing with MySQL

Conclusion

This paper presents a cost-effective and reliable student attendance management system that integrates Radio Frequency Identification (RFID) with facial recognition on an embedded Raspberry Pi platform. The proposed system automates the traditionally manual process of attendance logging, reducing time consumption and the potential for proxy or fraudulent attendance. By combining RFID technology for identification and facial biometrics for verification, the system ensures dual-layer authentication, enhancing both accuracy and security. The use of OpenCV’s Haar Cascade and LBPH algorithms provides efficient, real-time face detection and recognition suitable for classroom-scale deployments. Additionally, the integration of SQLite and MySQL enables both local and centralized data storage with robust notification capabilities.

References

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Copyright

Copyright © 2025 Ashrithaa Kulkarni. 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.