This project demonstrates face detection using OpenCV's Haar Cascade classifier. The classifier used is haarcascade_frontalface_default.xml, which is specifically trained to detect frontal faces in images.
Face detection is a computer vision task that identifies and locates human faces in digital images. This project uses OpenCV, an open-source computer vision library, to perform face detection. The haarcascade_frontalface_default.xml file is a pre-trained model provided by OpenCV that detects frontal faces using the Haar feature-based cascade classifier technique.
To run this project, you need to have Python and OpenCV installed. You can install the necessary dependencies using pip:
pip install opencv-python You will also need to download the haarcascade_frontalface_default.xml file, which is available in the OpenCV GitHub repository or directly from your OpenCV installation path. Ensure you have the haarcascade_frontalface_default.xml file in your project directory. If not, you can download it from here.
Run the face detection script:
python face_detection.py
The script will process the images or video stream and display the detected faces.
Video Reference: The `
This project is licensed under the MIT License. See the LICENSE file for details.