OpenCV Face and Hand Gesture Recognition

Overview

This project implements a facial recognition system with hand gesture detection using OpenCV and MediaPipe. It includes functionalities to create a dataset, train a model, and test it in real-time with a webcam.

Features

• Face detection using OpenCV's Haar cascades.
• Hand gesture recognition using MediaPipe.
• Training a face recognition model with LBPH (Local Binary Pattern Histogram).
• Real-time face and hand gesture detection.

Installation

Prerequisites

Ensure you have Python installed along with the following dependencies:

pip install opencv-python numpy mediapipe pillow

Usage

1. Create Dataset

To capture face images and create a dataset, uncomment the create_dataset() function in opencv.py and run:

python opencv.py

Press q to stop capturing after collecting sufficient images.

2. Train Model

Train the face recognition model by running:

python opencv.py

This will generate a model.yml file containing the trained model.

3. Test Model

Test real-time face recognition with hand gesture-based identification by running:

python opencv.py

Press q to exit the test mode.

File Structure

OpenCV/
│── faceDataset/       # Folder containing captured face images
│── model.yml          # Trained face recognition model
│── opencv.py          # Main script for dataset creation, training, and testing
│── README.md          # Documentation

Dependencies

• Python
• OpenCV
• NumPy
• Pillow
• MediaPipe