🚀 Face Recognition Project

📋 Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• How It Works
• Demo
• Contributing
• License
• Acknowledgments

🌟Overview

The Face Recognition Project is a web-based application that utilizes advanced machine learning techniques to detect and recognize faces in images and video streams. This project aims to provide a robust solution for various applications, including security systems, user authentication, and interactive user experiences.

Features

• Real-time Face Detection: Detect faces in real-time using webcam input.
• Face Recognition: Identify and recognize faces from a database of known individuals.
• User-Friendly Interface: Intuitive UI for easy interaction and navigation.
• Multiple Recognition Models: Support for various machine learning models to enhance accuracy.
• Image Upload: Users can upload images for face recognition.
• Cross-Browser Compatibility: Works seamlessly across modern web browsers.

📖Technologies Used

• JavaScript: Core programming language for the application.
• HTML/CSS: For structuring and styling the web application.
• TensorFlow.js: A library for machine learning in JavaScript, used for face detection and recognition.
• OpenCV.js: A JavaScript binding for OpenCV, used for image processing tasks.
• Node.js: Backend server for handling requests (if applicable).
• Bootstrap: For responsive design and layout.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/webermayank/Face_Recognise_JS.git

2. Navigate to the project directory:

   cd Face_Recognise_JS

3. Open the index.html file in your preferred web browser. You can also use a local server for better performance:

   npx http-server .  # Requires http-server package

🎯 Usage

1. Upload an Image: Click on the upload button to select an image from your device.
2. Use Webcam: Click on the "Start Webcam" button to begin real-time face detection.
3. View Results: Detected faces will be highlighted, and recognized individuals will be displayed with their names.

📚How It Works

The application uses a combination of computer vision and machine learning techniques:

• Face Detection: Utilizes Haar Cascades or DNN models to locate faces in images.
• Feature Extraction: Extracts facial features using pre-trained models.
• Face Recognition: Compares extracted features against a database of known faces to identify individuals.

Demo

A live demo of the project can be found at Demo Link (replace with actual link).

💡Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit them (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• TensorFlow.js: For providing powerful machine learning capabilities.
• OpenCV.js: For enabling advanced image processing techniques.
• Contributors: Thank you to all contributors who have helped improve this project.
• Inspiration: Inspired by various face recognition technologies and research papers.