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Face Recognition System

This project is a Face Recognition System that allows users to add new faces to a dataset, train a classifier on the dataset, and recognize faces using a pre-trained classifier. It consists of three main Python scripts:

  1. main.py - The entry point for the system, providing a menu for adding new users, training the classifier, and recognizing faces.
  2. dataset_generator.py - Handles dataset generation by capturing images from the webcam, preprocessing them, and saving them for training.
  3. face_recognizer.py - Recognizes faces using the trained classifier and displays the recognized name and confidence level on the screen.

Features

  • Add New User: Capture images of a new user, add their details (name and ID) to the dataset, and update the classifier.
  • Train Classifier: Automatically trains the classifier on the new dataset.
  • Recognize Face: Uses the webcam to detect and recognize faces in real-time, displaying the recognized name on the screen.

Requirements

  • Python
  • OpenCV
  • NumPy
  • PIL (Pillow)

Setup and Installation

  1. Clone the repository: 
    git clone https://github.com/yourusername/facerecognition.git
cd facerecognition
  2. Install the required Python packages: 
    pip install opencv-contrib-python numpy pillow
  3. Download the Haar Cascade XML file for face detection:
    • You can download the haarcascade_frontalface_default.xml file from the OpenCv GitHub repository.
    • Place the file in the root directory of the project.
  4. Run the program: 
    python main.py

Project Structure

  • main.py: The main script that provides a command-line interface for adding new users, training the classifier, and recognizing faces.
  • dataset_generator.py: Handles the dataset generation and training of the classifier.
  • face_recognizer.py: Performs real-time face recognition using the webcam.
  • user_data.json: A JSON file that stores user data (name and ID) for recognition purposes.
  • classifier.xml: The trained classifier file used for face recognition.

Usage

  1. Add New User:

    • Run the script using python main.py.
    • Choose option 1 to add a new user.
    • Enter the name and ID of the user.
    • The system will guide you through capturing 30 images of the user's face. Follow the on-screen instructions.
    • The images will be saved, and the classifier will be trained automatically.

  2. Recognize Face:

    • Run the script using python main.py.
    • Choose option 2 to recognize a face.
    • The system will use the webcam to detect and recognize faces in real-time, displaying the name and confidence level on the screen.

  3. Exit:

    • Choose option 3 to exit the program.

JSON Data Storage

  • The user data (name and ID) is stored in a user_data.json file as key-value pairs. The key is the ID, and the value is the name of the user.

Important Notes

  • Ensure that your webcam is connected and working properly before running the program.
  • The system requires good lighting conditions to perform accurate face recognition.
  • The confidence level of recognition depends on the quality and number of images in the dataset.

License

This project is licensed under the MIT License.

Author

Acknowledgments

  • This project uses the OpenCV library for face detection and recognition.
  • The Haar Cascade classifier used in this project is provided by OpenCV.