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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.

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