Face-Detection-Using-OpenCV

A real-time Face Detection System built using OpenCV and Haar Cascade Classifier. This system captures live video from the webcam, processes the video frames to detect faces, and visually highlights the detected faces with rectangles in real-time.

Features

-Real-Time Face Detection: Detects faces from a live video feed.

-Haar Cascade Classifier: Uses OpenCV's pre-trained Haar Cascade classifier to identify faces.

Required Libraries:

opencv-python - for computer vision functionalities

Run Locally

Clone the project

git clone https://github.com/viso27/Face-Detection-Using-OpenCV.git

Go to the project directory

cd Face-Detection-Using-OpenCV

Install dependencies

pip install opencv-python numpy

Run the script

python face_detection.py

How It Works

-Webcam Capture: The script captures video from the default webcam using cv2.VideoCapture(0).

-Haar Cascade Classifier: A pre-trained Haar Cascade model is loaded (haarcascade_frontalface_default.xml) for face detection.

-Grayscale Conversion: The frame is converted to grayscale using cv2.cvtColor(), as face detection works better on grayscale images.

-Face Detection: The detectMultiScale() method detects faces, and rectangles are drawn around them using cv2.rectangle().

-Real-Time Display: The processed frame is displayed in a window, and it updates in real time.

Output