Capturing the Happiness: Real-time Facial Expression Recognition with OpenCV

Overview

The "Capturing the Happiness" project utilizes OpenCV to perform real-time facial expression recognition, focusing on detecting smiles. The system captures video from the default camera, identifies faces using Haar cascades, and further detects smiles within the detected faces.

Dependencies

Make sure you have the following dependencies installed:

• Python 3.x
• OpenCV (pip install opencv-python)
• Haar Cascade XML files for face and smile detection (included in the dataset folder)

Usage

Clone the repository:

git clone https://github.com/j4ck4l-24/capturing_the_happiness/tree/main

cd capturing_the_happiness

pip install opencv-python numpy

python capturing_happiness.py

Haar Cascades

The project uses Haar cascades for face and smile detection. The pre-trained Haar cascade XML files are included in the dataset directory. You can replace them with more accurate or specialized cascades if needed.

• Face Cascade: haarcascade_frontalface_default.xml
• Smile Cascade: haarcascade_smile.xml

Image Capture

Captured images are saved in the project directory. The filenames are in the format n.jpg, where n is an incrementing number starting from 500.

Customization

Feel free to experiment and customize the parameters in the script (capturing_happiness.py) to improve face and smile detection based on your environment. You may consider adjusting the following parameters:

• detectMultiScale parameters for both face and smile detection
• Colors and thickness of rectangles used for visualizing face and smile detection in the live video stream
• Image capture conditions, such as the number of images to capture (cnt >= 503 in the provided script)