Implementation of Face Recognition Automated Attendance Management System using ESP32-CAM

Abstract

Attendance management is a crucial task in educational institutions and organizations. Traditional methods of recording attendance, such as manual entry or card-based systems, are time-consuming, error-prone, and difficult to manage in large-scale environments. To overcome these challenges, this paper presents an automated attendance management system using the ESP32-CAM microcontroller integrated with face recognition technology. The system captures real-time images, processes them using a Haar Cascade Classifier for face detection, and employs Principal Component Analysis (PCA) for face recognition. Upon successful recognition, the system automatically logs the attendance into a centralized database. The ESP32-CAM’s low cost, compact size, and wireless connectivity make it an ideal solution for large-scale deployment. The proposed system achieved an accuracy of approximately 95% with a low false positive rate, demonstrating reliable performance even under varying lighting conditions. This solution reduces human effort, improves accuracy, and ensures real-time attendance monitoring, making it suitable for educational institutions and professional environments.

Introduction

Overview:
Efficient attendance management is vital in educational and corporate settings. Traditional methods (manual sheets, RFID, biometrics) are often time-consuming, error-prone, and vulnerable to manipulation (e.g., proxy attendance). This has led to a growing demand for automated, accurate, and contactless solutions.

Face recognition technology offers a non-invasive, hygienic, and scalable method of biometric identification, especially useful in the post-COVID era. It ensures real-time, high-accuracy recognition, even in large environments.

2. Proposed System with ESP32-CAM:

The system leverages the ESP32-CAM, a low-cost, Wi-Fi-enabled microcontroller with an integrated camera. Key features include:

• Face Detection: Using Haar Cascade Classifier.

• Face Recognition: Using Principal Component Analysis (PCA) for dimensionality reduction and efficient feature extraction.

• Real-Time Processing: Captures and identifies faces quickly upon entry.

• Attendance Logging: Stores data in a centralized database with real-time cloud sync.

• Contactless and Automated: Requires no manual input or validation, reducing human error and fraud.

3. System Architecture & Methodology:

A. Hardware Components:

• ESP32-CAM: For capturing and processing images.

• NodeMCU (ESP8266): For Wi-Fi communication and system control.

• Optional Display (LCD/OLED): For user feedback (e.g., “Attendance Recorded”).

B. Software & Libraries:

• Arduino IDE for development.

• OpenCV for image processing.

• Required Libraries: Wire.h, LiquidCrystal.h, SoftwareSerial.h, etc.

C. Face Recognition Workflow:

1. Training: Enroll users by capturing images under various lighting conditions.

2. Recognition: Extract facial features from live images and match them with stored vectors.

3. Logging: Log attendance (user ID, date, time) locally (SD card) or to a cloud service.

4. Display: Provide immediate feedback via display module.

D. Additional Features:

• Error Handling for failed recognition or connection issues.

• Data Export & Integration with cloud platforms like Firebase or MQTT for real-time monitoring and remote access.

4. Performance & Results:

• Accuracy:

  • 94.5% in optimal lighting.

  • 89.2% in low light.

• Speed: Average recognition time of 250ms.

• Reliability:

  • False Acceptance Rate (FAR): 1.8%

  • False Rejection Rate (FRR): 2.3%

• Stable operation in various lighting and network conditions.

• Real-time logging with precise timestamps.

5. Literature Insights:

Studies support the use of ESP32-CAM for facial recognition due to its:

• Low power usage

• Wireless connectivity

• Compact size and cost-efficiency

Challenges noted include:

• Limited memory/computational power

• Difficulty in low-light or obstructed conditions

• Security and data privacy, necessitating encryption and secure protocols

Hybrid models combining on-device processing with cloud analytics are recommended for improved performance.

Conclusion

In conclusion, this paper presents the design and implementation of a Face Recognition Automated Attendance Management System using the ESP32-CAM. The proposed system demonstrates how low-cost, efficient hardware can be combined with advanced face recognition algorithms to automate attendance tracking in real-time. The system’s accuracy, ease of deployment, and low power consumption make it a promising solution for educational institutions, corporate offices, and any large-scale organization looking to streamline and modernize their attendance management process. The implementation of a Face Recognition Automated Attendance Management System using ESP32-CAM has successfully demonstrated the potential of integrating computer vision with embedded systems for real-time attendance tracking. The use of machine learning algorithms for face recognition ensures high accuracy and security. This system not only eliminates manual attendance processes but also reduces human errors and enhances overall productivity. Additionally, the system\'s real-time functionality and ease of deployment offer significant advantages over traditional methods. Future improvements could include adding features such as multi-face recognition, integration with cloud-based storage, and further optimization of processing time for larger-scale deployments..

References

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Copyright

Copyright © 2025 Dasari Meena, P. Parimalarani, N. Veerendra Kumar, Sk. Farukh, M. Revanth Babu, P. Ekshitheswari. 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.