Real Time Face Detection

Overview

This project implements a real-time face detection system using OpenCV and machine learning techniques. The system is designed to identify and locate faces in a live video feed, providing an efficient and interactive way to perform face detection.

Tech Stack

• Programming Language: Python
• Frameworks and Libraries:
  • OpenCV
  • NumPy
  • Matplotlib
  • dlib
• Tools:
  • Jupyter Notebook

Features

• Real-Time Detection: Detects faces in live video streams.
• Efficient Algorithms: Uses pre-trained models for accurate and fast face detection.
• Visualization: Draws bounding boxes around detected faces in the video feed.
• Scalability: Can be adapted for various applications, including security and user interaction.

Installation

1. Clone the repository:

   git clone https://github.com/TilakSanghvi/Real_Time_Face_Detection.git

2. Navigate to the project directory:

   cd Real_Time_Face_Detection

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Open the Jupyter Notebook:

   jupyter notebook Real_Time_Face_Detector.ipynb

2. Follow the instructions in the notebook to start the real-time face detection system.

Contributing

Contributions are welcome! Please follow these steps to contribute:

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

License

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

Contact

For any inquiries, please contact Tilak Sanghvi at tilakcsanghvi@gmail.com.