Face Detection implemented with Python using OpenCV

Face detection is a computer vision technology being used in a variety of applications that identifies human faces in digital images. With the advent of technology, face detection has gained a lot of importance especially in fields like photography, security, and marketing.

What is OpenCV

OpenCV is a great tool for image processing and performing computer vision tasks. It is an open-source library that can be used to perform tasks like face detection, objection tracking..

Advantages of OpenCV:

• OpenCV is free and an open-source library.
• OpenCV is fast as it is written in C/C++ language.
• With less system RAM, OpenCV works better.
• It supports most of the operating systems like Windows, Linux, and macOS.

Installation:

To install OpenCV you can use this command:

pip install opencv-python

Implementation

Face detection using Haar cascades is a machine learning based approach where a cascade function is trained with a set of input data. OpenCV already contains many pre-trained classifiers for face, eyes, smiles, etc..
You can get other Haar cascades from this link.

Face detection in Image:

First we need to load face-cascade.

cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

The detection works only on grayscale images. So it is important to convert the color image to grayscale.

cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

detectMultiScale function is used to detect the faces. It takes 3 arguments, the input image, scaleFactor and minNeighbours. you can read about it here.

detectMultiScale(gray_scaled_img, scaleFactor=1.2,minNeighbors=5,minSize=(20,20))

Face detection using a Webcam:

First we need to inisialize the Webcam.

webcam = cv2.VideoCapture(0)

Now we need to add a loop for detect faces in each frame.

...
while True:
    frame = webcam.read()
	
    gray_scaled_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	
    face_coordinates = trained_face_data.detectMultiScale(gray_scaled_img, scaleFactor=1.2,minNeighbors=5,minSize=(20,20))
	
    for(x, y, w, h) in face_coordinates:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 10)
...