Automatic Face Recognition Attendance System Using ESP32-CAM Module

Overview

This project is a face recognition-based attendance system developed using the ESP32-CAM module and Python's OpenCV library. The system detects faces in real-time and marks attendance for recognized individuals. This project aims to automate the attendance marking process, making it more efficient and less prone to errors.

Features

• Real-Time Face Detection: The system can detect faces in real-time using the ESP32-CAM module.
• Face Recognition: The system identifies individuals from the detected faces using a pre-trained model.
• Automated Attendance Marking: Attendance is automatically marked for recognized individuals and stored in a database or file.
• User-Friendly Interface: The project includes a simple interface for monitoring and managing attendance records.

Components

• ESP32-CAM Module: The core hardware component responsible for capturing images and streaming video.
• Python: Used for developing the face recognition and attendance marking logic.
• OpenCV: A computer vision library used for face detection and recognition.

How It Works

1. Image Capture: The ESP32-CAM module captures images or streams video.
2. Face Detection: The captured images are processed using OpenCV to detect faces.
3. Face Recognition: Detected faces are compared against a pre-trained model to identify the individual.
4. Attendance Marking: Once a face is recognized, the system automatically marks the attendance for that individual by logging the time and date.
5. Data Storage: The attendance data is stored in a database or file for future reference.

Prerequisites

• Hardware:
  • ESP32-CAM module
  • USB to TTL converter for programming
  • Power supply (5V)
• Software:
  • Arduino IDE or PlatformIO for programming the ESP32-CAM
  • Python 3.x
  • OpenCV library (opencv-python)
  • Any additional Python libraries needed for data handling and storage

Setup Instructions

Hardware Setup

1. Connect the ESP32-CAM module to the USB to TTL converter for programming. Connect the power supply as well.
2. Upload the ESP32-CAM code using Arduino IDE or PlatformIO. Ensure that the camera and Wi-Fi settings are correctly configured.
3. Deploy the ESP32-CAM module at the desired location for capturing faces.

Software Setup

1. Install Python and OpenCV:

   pip install opencv-python

2. Clone the project repository and navigate to the project directory:

   git clone <your-repo-link>
   cd face-recognition-attendance-system

3. Run the Python script to start the face detection and recognition process:

   python attendance_system.py

4. View the attendance records stored in the specified file or database.

Customization

• Training the Model: If you want to use your own dataset, you can train a new face recognition model using OpenCV or any other machine learning framework.
• Data Storage: Modify the data storage logic in the Python script to store attendance records in a database, CSV file, or any other format as per your requirement.

Future Improvements

• Enhanced Recognition Accuracy: Implement advanced machine learning algorithms to improve face recognition accuracy.
• Multiple Camera Support: Extend the system to support multiple ESP32-CAM modules for larger areas.
• Mobile App Integration: Develop a mobile app for easier access to attendance records.

Conclusion

This ESP32-CAM based face recognition attendance system is an efficient and automated solution for tracking attendance. By leveraging the power of Python and OpenCV, it provides a robust method for real-time face recognition and attendance marking.

Author

• [Mausam Raj]

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