This project offers a robust solution for real-time face recognition using IP cameras, integrating technologies like Django, Vue.js, DeepFace, OpenCV, and MediaPipe. The core feature allows for real-time logging of recognized faces against a pre-registered database of users.

• User Registration: Register users and store their facial information for later recognition.
• IP Camera Integration: Link IP cameras to the system for real-time face tracking.
• Real-time Face Recognition: Every face captured by the IP cameras is compared against the user database in real-time.
• Automatic Logging: If a recognized face matches a registered user, the information gets logged in real-time. Faces that aren't in the database are ignored.

• Backend: Django, Flask
• Frontend: Vue.js
• Face Recognition: DeepFace, OpenCV, MediaPipe

• camera_track_backend: Contains all backend code, built with Django.
• camera_track_frontend: Contains frontend code, developed using Vue.js.

To run this project locally, you need to set up both frontend and backend.

Navigate to the camera_track_frontend directory:

cd camera_track_frontend
npm run serve -- --port 4000

Ensure you have poetry installed. Navigate to the camera_track_backend directory:

cd camera_track_backend
poetry run python manage.py runserver localhost:8000

This will start the backend server on http://localhost:8000/

Contributions to DeepFaceRegistry are welcome! Here's how you can contribute:

Fork the repository on GitHub. Create a new branch for your proposed feature or fix. Commit your changes with an informative description. Push your branch and submit a pull request. We appreciate your input!

DeepFaceRegistry is open source software licensed under the MIT License. See the LICENSE file for more details.