Face Recognition based Attendance Management System

Abstract

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. Nowadays, the use of biometrics like face recognition system has been crucial in authentication concretely in attendance process. The system’s goal is to make the attendance marking process quick and easy because the teacher’s tedious job in class is time consuming in monitoring the students while marking attendance and ensuring that no fake attendance is marked. To solve the problem efficiently, the system employs a machine approaching rigorous attendance verification. The purpose of a face recognition system is to automate the recordings of employees, eliminating the need for manual clock-ins or time-sheets. This system ensures accurate attendance records, reducing the chances of errors or buddy punching. This attendance process saves time and resources for HR departments and teachers. This system provides a contactless solution for attendance in situations where hygiene and safety are paramount. These systems can be used in various settings, including workplaces, educational institutions, events, and even healthcare facilities

Introduction

1. Background and Motivation

• COVID-19 accelerated the adoption of digital technologies, including remote work and online education.

• Traditional attendance systems (manual or paper-based) are inefficient, error-prone, time-consuming, and lack real-time access.

• To address these issues, digital attendance systems, particularly those using facial recognition, offer improved accuracy, security, transparency, and efficiency.

2. Digital Attendance via Facial Recognition

• Facial Recognition Attendance Systems automate the attendance process by detecting and recognizing faces, recording entry/exit times without manual input.

• Benefits include:

  • Elimination of buddy punching (proxy attendance).

  • Time-saving and cost-efficient.

  • Enhanced data security and accessibility for administrators.

  • Useful for students and employees alike.

3. Literature Review of Related Technologies

A. RFID + Face Recognition

• Hybrid system using RFID for quick check-in and facial recognition for identity verification.

• Increases accuracy and reduces proxy attendance.

B. Iris Recognition

• Highly secure method using unique iris patterns for authentication.

• Offers excellent precision and is resistant to forgery.

C. Machine Learning-Based Recognition

• Uses ML models like Viola-Jones, HOG, and SVM/CNN for real-time, accurate facial recognition.

• Learns and adapts over time for improved results.

D. Eigenfaces & Fisherfaces

• Traditional techniques using PCA (Eigenfaces) and LDA (Fisherfaces) for dimensionality reduction and classification.

• Useful for understanding earlier approaches before deep learning.

E. Hybrid Model: DWT + DCT + RBF

• Combines wavelet and cosine transforms for feature extraction, followed by RBF networks for classification.

• Offers high performance under challenging conditions (lighting, occlusions, etc.).

4. Proposed System Methodology

A. Dataset Creation

• Collects facial images of users and stores them with identifiers for future comparison.

B. Face Detection

• Uses algorithms like Haar cascades to identify facial regions in live video streams.

C. Face Recognition

• Uses techniques such as LBPH and CNN to extract and compare facial features with stored data.

D. Attendance Update

• If a face match is found, attendance is marked with a timestamp and stored in a database.

5. System Implementation

Key Tools & Technologies

• OpenCV: For image processing and face detection.

• HTML/CSS + React: For user-friendly, real-time web interfaces.

• Flask: Backend logic for processing and routing data.

• Face Recognition Library: Uses pre-trained models for facial encoding and matching.

• MySQL: Stores facial data and attendance logs securely.

• Real-Time Components:

  • Threading & Scheduling: Allows simultaneous video capture and data processing.

  • Flask-SocketIO: Enables live updates on the user interface.

Conclusion

The facial recognition-based attendance management system is an innovative and effective solution designed to overcome the limitations of traditional attendance methods. Manual attendance systems are usually slow, can easily have mistaken, and make it easier for students or employees to fake their presence. This system transforms attendance tracking by using biometric technology specifically, facial recognition to automate the entire process in a contactless and secure manner. By capturing live video, detecting faces, and matching them with pre-registered data, the system automatically marks attendance and records timestamps accurately. This eliminates the need for manual clock-ins, paper-based records, or physical ID cards. It not only improves accuracy but also significantly reduces administrative workload for teachers, HR departments, and other staff. Additionally, it ensures transparency and real-time monitoring of attendance records, which can be accessed remotely. The use of technologies such as OpenCV, face recognition libraries, Flask, MySQL, and HTML/CSS/React allows for the development of a responsive, interactive, and user-friendly platform. These tools work together to create a system that can recognize faces under varying conditions, such as different lighting, angles, or facial expressions. Real-time frameworks like Flask-SocketIO further enhance user experience by instantly displaying recognition results on the interface. One of the major benefits of this system is its suitability for post-COVID environments, as it offers a contactless solution that supports health and hygiene protocols. It also minimizes the risk of data manipulation and enhances security through biometric verification. Beyond educational institutions, it has broad applications in offices, healthcare, law enforcement, retail, and secure facilities. The future scope of this technology is promising. It can be enhanced by integrating advanced features such as liveness detection to prevent spoofing, multi-factor authentication for added security, and mobile apps for easier access and notifications. Cloud deployment can make it scalable across multiple locations, and more advanced AI models like deep learning and 3D recognition can further improve accuracy. In summary, the facial recognition attendance system offers a powerful combination of accuracy, efficiency, security, and adaptability. It modernizes attendance tracking, reduces manual effort, prevents fraudulent entries, and brings convenience to users. With its wide range of applications and potential for future enhancements, it represents a significant step toward smarter, safer, and more reliable attendance management in today’s digital world.

References

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Copyright

Copyright © 2025 Om Bhakat, Poulami Roy, Poushmita Paul, Prabhat Ranjan, Prachi Shaw, Avali Banerjee, Koushik Pal, Soma Boral. 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.