Skip to content

Demonstration of different algorithms and operations on faces. Join the Discord channel for discussion.

License

Notifications You must be signed in to change notification settings

utkarsh147-del/Face-X

 
 

Repository files navigation

Issues Pull Requests Forks Stars License

Join official Discord Channel for discussion.

forthebadge forthebadge forthebadge

Demonstration of different algorithms and operations on faces

Despite the availability of a variety of open source face recognition algorithms, there are no ready-made solutions which can be implemented directly. This project demonstrates all kinds of algorithms and various operations that can be implemented on a frontal face. The available algorithms process only high-resolution static shots and perform sufficiently well.


There are several approaches for an algorithm to recognize a face. An algorithm can make use of statistics, try to find a pattern which represents a specific person or use a Convolutional Neural Network (CNN).

⭐ How to get started with open source?

You can refer to the following articles on the basics of Git and Github.


💥 How to Contribute to Face-X?

  • Take a look at the Existing Issues or create your own Issues!
  • Wait for the Issue to be assigned to you.
  • Fork the repository

click on the uppermost button

  • Clone the repository using-
git clone https://github.com/akshitagupta15june/Face-X.git

Installation 👇

  1. Create virtual environment
python -m venv env
  1. Linux
source env/bin/activate

OR

  1. Windows
env\Scripts\activate
  1. Install
pip install -r requirements.txt

Face-X is a part of these open source programs❄



Get Started with Open Source programs 👨‍💻

Start Open Source an article by Anush Krishna

❤️ Project Admin


akshitagupta15june

👑 Admin

🌟 Contributors

Thanks goes to these wonderful people ✨✨:

About

Demonstration of different algorithms and operations on faces. Join the Discord channel for discussion.

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 79.5%
  • Python 20.3%
  • Other 0.2%