Skip to content

Latest commit

 

History

History
82 lines (54 loc) · 3.98 KB

README.md

File metadata and controls

82 lines (54 loc) · 3.98 KB

Facial Verification App 📷 🧐

This README file provides a detailed explanation of the Kivy facial verification app, outlining its functionalities, usage instructions, and dependencies .

Project Overview 🎨

This application leverages Kivy, OpenCV, and DeepFace to create a real-time facial verification system using your webcam . It captures frames from the camera, detects faces using DeepFace's superpowers , and compares them against a predefined database (not included in this code). If a match is found, the user's identity is displayed which can be processed to do further functions. Otherwise, a red message indicates a no match or face detection failure.

Features 🚀

  • Real-time webcam capture
  • Face detection using DeepFace's magic ✨
  • Facial verification against a user database (needs database of image for each user inside the train repo, )
  • Visual feedback with success/failure messages and color changes (green for success, red for errors )

Dependencies

Installation

  1. Ensure you have Python (version 3.x recommended) installed on your system (check with python --version in your terminal).

  2. Create a virtual environment to manage dependencies (optional but recommended, keeps things tidy! ).

  3. Install the required libraries using pip:

    pip install kivy opencv-python deepface

  4. (Optional) Install pandas and numpy if you intend to modify the face detection functionality.

Usage 🖥️

  1. Create a 'Train' repository in the main directory and include a class of users which contain image of the user. do this for each users.(not included in this code, but you can set it up later! )

  2. (Optional) resize the iamges inside each repo to the same shape. This will help the model learn on a uniform data.

    python img_resizer.py

  3. Run the application from your terminal:

    python main.py

Code Structure

The code is organized into the following classes and functions:

  • CamApp class:

    • Initializes the application, including webcam capture and layout elements (creates the user interface ).
    • Provides methods for updating the webcam image, face detection, verification, and label reset (keeps things running smoothly! ).

  • face_detect function:

    • Uses DeepFace to detect faces in an image and compare them against the database (needs implementation, but the code is ready to accept your custom logic! ).
    • Returns the identified user's information or a message indicating no match or face detection failure.

  • verify function:

    • Captures a frame from the webcam, saves it as an image, performs face detection, and updates the verification label based on the outcome (the verification magic happens here! ✨).

  • reset_verification_label function:

    • Resets the verification label to its default state (green text, "Welcome") after a verification attempt (clears the message after verification is done! ).

Additional Notes

  • The code assumes you have a pre-defined user database set up for verification.
  • You might need to adjust the DeepFace model selection (models[0]) based on your requirements and installation.
  • Consider error handling and logging for robustness (makes the code more reliable ).

Contributing 💡

Contributions are welcome! If you have any ideas for improvements or new features, feel free to open an issue or submit a pull request.

Credits 🙌

This project was created by Dawit Neri

Support 💬

If you encounter any issues or have any questions, feel free to reach out to [email protected] or open an issue in the GitHub repository.