Face and Smile Detection with OpenCV: Real-Time and Static Image Applications

This project demonstrates how classical Haar Cascade classifiers in OpenCV can be used to detect faces, eyes, and smiles in both static images and real-time webcam feeds.

Project Overview

This project includes two Python scripts:

1. Static Image Detection
   Detects faces, eyes, and smiles from a given image.

2. Webcam Real-Time Detection
   Continuously detects facial features from a live webcam feed using OpenCV.

Repository

GitHub Repository:
https://github.com/TaufiaHussain/Face-webcam-Detection

Features

• Face detection
• Eye detection
• Smile detection
• Real-time camera input (OpenCV)
• Image-based input and saving output with annotations

How to Run

1. Clone the repository:

git clone https://github.com/TaufiaHussain/Face-webcam-Detection
cd Face-webcam-Detection

2. Install dependencies:

pip install opencv-python

3. Run either script:

Static Image:

python "face eye smile detection.py"

Webcam Detection:

python "webcam detection.py"

Note: Press q to stop the webcam.

Method Used

Haar Cascades are classical machine learning models trained on positive/negative samples for object detection.

No deep learning models were used — just efficient, lightweight, and fast OpenCV-based cascades.

Output Samples

face_eye_smile_detected.jpg is automatically saved when using the static image script.

Webcam detection displays bounding boxes in real-time with the OpenCV GUI.

Applications

-Educational demos for beginners in computer vision

-Real-time smile detection

-Face tracking for UI interactions

License

This project is licensed under the MIT License. See the repository for full details.

Author

Taufia Hussain
Scientific researcher | Co-Founder@DataLens.Tools
https://github.com/TaufiaHussain | https://www.linkedin.com/in/taufia-hussain-52300015/