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Face Recognition App

Overview

The Face Recognition App is a desktop application designed to facilitate face recognition using OpenCV. This application allows users to collect face samples, train a face recognition model, and recognize faces based on the trained model. The user-friendly interface is built using Tkinter, and threading is utilized to keep the GUI responsive during long-running tasks such as sample collection, model training, and face recognition.

Features

  • Sample Collection: Collect face samples for a given name.
  • Model Training: Train a face recognition model using the collected samples.
  • Face Recognition: Recognize faces based on the trained model.
  • Tooltips: Hover tips for each button to guide the user.

Prerequisites

  • Python 3.x
  • OpenCV
  • Numpy
  • Tkinter

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/face-recognition-app.git
cd face-recognition-app

  2. Install the required packages:

    pip install -r requirements.txt

  3. Ensure OpenCV is installed:

    pip install opencv-python opencv-contrib-python

Usage

  1. Run the application:

    python app.py

  2. Enter Your Name:

    • Enter your name in the text box provided.

  3. Collect Samples:

    • Click the "Collect Samples" button to start collecting face samples.
    • Ensure your face is visible in the webcam.
    • The process will collect 100 samples and save them to the specified directory.

  4. Train Model:

    • Click the "Train Model" button to train the face recognition model with the collected samples.
    • Wait for the training process to complete.

  5. Recognize Faces:

    • Click the "Recognize Faces" button to start face recognition.
    • Ensure your face is visible in the webcam.
    • The application will display the confidence level and recognize the face if it matches the trained model.

Project Structure

  • app.py: The main application script containing the GUI and the core functionality.
  • requirements.txt: List of required packages.
  • image_samples/: Directory where the collected face samples are stored.

Functions

collect_face_samples(name, data_path)

Collects face samples using the webcam and saves them to the specified directory.

  • Parameters:
    • name: The name of the person whose samples are being collected.
    • data_path: The path to the directory where the samples will be saved.

train_face_recognition(data_path)

Trains a face recognition model using the collected samples.

  • Parameters:

    • data_path: The path to the directory where the samples are stored.

  • Returns:

    • model: The trained face recognition model.

recognize_face(model, name)

Recognizes faces using the trained model.

  • Parameters:
    • model: The trained face recognition model.
    • name: The name of the person to be recognized.

GUI Components

  • Labels:

    • "Enter Your Name": Label for the name entry field.
    • Status Label: Displays the current status of the application.

  • Entry:

    • Name Entry: Field for entering the user's name.

  • Buttons:

    • "Collect Samples": Button to start sample collection.
    • "Train Model": Button to start model training.
    • "Recognize Faces": Button to start face recognition.

  • Tooltips:

    • Hover tips for each button to guide the user.

Threading

  • Threading is used to keep the GUI responsive during long-running tasks such as sample collection, model training, and face recognition. Each of these tasks is run in a separate thread to avoid freezing the GUI.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes or enhancements.

License

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

Acknowledgements

  • This project uses the OpenCV library for computer vision tasks.
  • Tkinter is used for the graphical user interface.

Feel free to customize this README to fit the specific details and requirements of your project.