rishraks/Face_Recognition

A Python-based Face Recognition project utilizing OpenCV, MediaPipe, and a trained machine learning model for real-time face detection and recognition. The system identifies individuals from live camera feeds with high accuracy, leveraging facial landmarks and bounding boxes to provide seamless predictions.

Face Recognition Project

This project is a Python-based real-time Face Recognition system that uses OpenCV, MediaPipe, and a machine learning model to detect and recognize faces. Users must collect data, process it, train the model, and then test it for face recognition.

Features

• Real-time face detection using MediaPipe Face Detection.
• Face landmark mapping with MediaPipe Face Mesh.
• Accurate face recognition powered by a user-trained machine learning model.
• Easily extensible for adding new individuals or improving recognition accuracy.

Installation

1. Clone the Repository:

   git clone https://github.com/your-repo/face-recognition-project.git
   cd face-recognition-project

2. Set Up Virtual Environment:

   python -m venv env
   source env/bin/activate  # On Windows: env\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Organize Data for Training:

   • Create a folder Data_Collection with subfolders named after each person's name.
   • Place corresponding images in each folder.

5. Train the Model:

   • Run the training script:

     python Data_Training.py

   • Save the generated model as model.p in the project directory.

Usage

Real-Time Face Recognition

After training the model, run the following command to start the application:

python Data_Testing.py

• Press Q to quit the application.

Training a New Model

To train a new model with your dataset:

1. Ensure the dataset is organized in the Data_Collection folder.
2. Run the training script:

   python training.py

3. Save the generated model as model.p.

Requirements

• Python 3.7+
• OpenCV
• MediaPipe
• NumPy
• Scikit-learn

File Structure

face-recognition-project/
├── Data_Collection/        # Dataset folder with subfolders for each person
├── model.p                # Trained model (generated by the user)
├── testing.py             # Script for real-time face recognition
├── training.py            # Script for training the face recognition model
├── requirements.txt       # Python dependencies
└── README.md              # Project documentation

Future Enhancements

• Add support for multiple cameras.
• Improve recognition accuracy with more training data.
• Implement GUI for user-friendly interaction.

Acknowledgements

This project leverages the following libraries and tools:

• OpenCV
• MediaPipe
• Scikit-learn

License

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