FaceRecognition

A lightweight real-time face detection & recognition app built with OpenCV / Node.js

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🚀 Project Overview

This project provides a straightforward solution for detecting and recognising faces in real time, using a webcam or camera feed.
It’s built with Node.js and opencv4nodejs (OpenCV bindings for Node), making it easy to extend, integrate and deploy.

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✨ Features

• Real-time face detection using your camera
• Face recognition against a set of known faces
• Lightweight and modular — easy to integrate into other systems
• Works across Windows / macOS / Linux (given OpenCV dependencies are satisfied)
• Can be extended for access control, attendance systems, automation projects, creative installations

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🧩 Tech Stack

• Language: Python
• Library: opencv-python mediapipe
• Platform: Desktop / Laptop (with webcam or external camera)
• License: MIT – free to use, modify, distribute

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🛠️ Installation & Setup

Run these commands in order

git clone https://github.com/Raziel206/FaceRecognition.git
cd FaceRecognition
pip install virtualenv
py -3.10 -m venv env
.\env\Scripts\activate
pip install -r requirements.txt
python -m src.main

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📁 Project Structure

FaceRecognition/
│
├── src/                  
│   ├── untils/
│   │      ├── __init__.py
│   │      ├── face_detect.py
│   │      ├── hand_detect.py
│   │      └── resizing.py
│   ├── __init__.py
│   ├── extension.py      
│   └── main.py    
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

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🎯 Use-Cases & Ideas

• Use for door-access system: recognise authorised faces and trigger unlock

• Use in classrooms / events: automatic attendance marking based on face recognition

• Use for creative installations: trigger lights/sounds when certain faces appear

• Use as a base for more advanced CV/AI work: add expression recognition, mask-detection, emotion tracking

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🔍 Tips & Best Practices

• Use clear, well-lit face images for each person in the known_faces/ folder — good data = better recognition

• Maintain consistent lighting and camera position for best accuracy

• Tune recognition thresholds (in recognizer logic) to avoid false positives or mis-matches

• If performance is slow, reduce camera resolution or adjust detection/recognition parameters

• Always test with multiple poses (frontal, slight angles) and different lighting conditions

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🤝 Contributing

Contributions are welcome and appreciated!
Whether it's a bug fix, feature improvement, documentation enhancement, or optimization — every contribution helps.

✅ How to contribute

1. Fork the repository
2. Create a new branch for your feature or bug fix

   git checkout -b feature-name

3. Make a pull request, or if it is a big change/major feature, create an issue.

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🙏 Acknowledgements

This project wouldn't be possible without the amazing work of the open-source community.
Special thanks to:

• Python — for being simple, fast to prototype, and developer-friendly
• OpenCV — the core of real-time computer vision in this project
• MediaPipe — for efficient, production-ready face detection & landmark tracking
• All contributors of these libraries for keeping them free, powerful, and evolving

Huge respect to open-source. This project stands on the shoulders of giants.

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📜 License

This project is licensed under the MIT License.

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