🧠 Gender & Hand Recognition with OpenCV & MediaPipe

A real-time computer vision application for gender detection using Deep Learning and hand gesture recognition using MediaPipe. Built with Python, OpenCV, and TensorFlow, this tool leverages webcam input for live detection and analysis.

🔗 Repository: GitHub Repo

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🛠️ Installation

1. Clone the repository:

git clone https://github.com/Syntax-Surfer-1/Real-Time-Gender-and-Hand-Recognition-with-OpenCV-and-MediaPipe.git
cd Real-Time-Gender-and-Hand-Recognition-with-OpenCV-and-MediaPipe

2. Install dependencies:

pip install opencv-python mediapipe numpy tensorflow deepface

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▶️ Usage

1. Run the main script:

python main.py

2. Controls:

• 🖥️ The webcam will open for real-time detection.
• ⛔ Press Esc to exit the application.

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📂 Project Structure

Real-Time-Gender-and-Hand-Recognition-with-OpenCV-and-MediaPipe/
├── main.py                 # Main execution script
├── models/                 # Trained deep learning models
├── utils/                  # Utility functions
├── images/                 # Image assets (if any)
├── requirements.txt        # Required Python packages
└── README.md               # Project documentation

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🤝 Contributing

Contributions are welcome! Follow the steps below to contribute:

1. Fork the repository

2. Create your feature branch:

   git checkout -b feature/YourFeature

3. Commit your changes:

   git commit -am 'Add your feature'

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request

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🙌 Credits

• 🔍 OpenCV — real-time computer vision library
• ✋ MediaPipe — hand tracking and pose estimation
• 🤖 TensorFlow — used for gender classification model
• 😃 DeepFace — face recognition and analysis

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📬 Contact

If you have any questions, suggestions, or feedback, feel free to contact:

📧 Email: [email protected]

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📌 Note: Make sure your webcam is connected and permissions are granted for full functionality.