Real-Time Face Recognition System Using OpenCV and LBPH Algorithm

Table of Contents

1. Project Overview
2. Technologies Used
3. Features
4. Installation
5. Usage
6. How It Works
7. File Structure
8. Contributing
9. License

Project Overview

This project implements a Real-Time Face Recognition System using OpenCV and the Local Binary Patterns Histograms (LBPH) algorithm. The system captures video input from a camera, detects faces in real-time, and recognizes individuals based on a pre-trained model. The primary goal of this project is to demonstrate the application of computer vision techniques for face recognition, providing a foundation for more complex implementations such as security systems, attendance management, and user authentication.

Technologies Used

• Python: The primary programming language for implementing the face recognition algorithms.
• OpenCV: A powerful library for computer vision tasks that provides tools for image processing and manipulation.
• NumPy: A library for numerical computing in Python, which aids in handling arrays and matrices for image data.
• scikit-learn: A machine learning library that supports the training and evaluation of models.
• Git: Version control system used to manage the project files.
• Git Large File Storage (LFS): A Git extension for managing large files efficiently.

Features

• Real-Time Face Detection: Utilizes Haar Cascades for detecting faces in live video streams.
• Face Recognition: Implements the LBPH algorithm to recognize faces from a pre-trained model.
• User-Friendly Interface: Displays recognized names on the video feed, making it easy to identify individuals.
• Train New Faces: Allows adding new faces to the model by providing images, which can be retrained for better accuracy.

Installation

Follow these steps to set up the project on your local machine:

1. Clone the Repository:

   git clone https://github.com/<username>/Real-Time-Face-Recognition-System-Using-OpenCV-Python-and-LBPH-Algorithm.git

2. Navigate to the Project Directory:

   cd Real-Time-Face-Recognition-System-Using-OpenCV-Python-and-LBPH-Algorithm

3. Install Required Packages: Make sure you have Python installed on your system. Install the required libraries using pip:

   pip install -r requirements.txt

   Note: If you don’t have a requirements.txt file, you can create one by using the following command to capture the current environment packages:

   pip freeze > requirements.txt

Usage

To run the Face Recognition System:

1. Ensure your camera is connected and functioning.

2. Run the main script:

   python main.py

3. The system will open a window displaying the live video feed with recognized faces. Press q to exit the program.

How It Works

1. Face Detection: The system uses Haar Cascade classifiers to detect faces in the video stream. These classifiers are pre-trained and can identify frontal faces in images.

2. Feature Extraction: Once a face is detected, the LBPH algorithm extracts features from the face image, which involves converting the image into a histogram representation based on local binary patterns.

3. Recognition: The extracted features are compared with those stored in the model. If a match is found, the recognized name is displayed on the video feed.

4. Training: To train the model with new faces, images of the individuals should be added to a designated folder, and the training script should be run.

File Structure

Real-Time-Face-Recognition-System-Using-OpenCV-Python-and-LBPH-Algorithm/
│
├── .gitignore             # Specifies files to be ignored by Git
├── README.md              # Project documentation
├── requirements.txt       # Required Python packages
├── Trainer.yml            # Configuration file for training settings
├── main.py                # Main script to run the face recognition system
├── train.py               # Script to train the model with new face images
├── data/                  # Directory containing training images
│   ├── person1/           # Images of person 1
│   ├── person2/           # Images of person 2
│   └── ...
└── models/                # Directory for storing trained models
    └── face_recognition_model.xml

Contributing

Contributions to this project are welcome! If you have suggestions for improvements, bug fixes, or new features, please feel free to submit a pull request or open an issue.

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes and push to your branch.
4. Submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

---

Feel free to modify any section to better fit your project's specifics! Let me know if you need any further adjustments or additional information!