Face Detection

What is Face Detection?

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.

What are Haar Cascades?

• Haar Cascade classifiers are an effective way for object detection.
• Proposed by Paul Viola and Michael Jones in their paper Rapid Object Detection using a Boosted Cascade of Simple Features ↗
• It is a machine learning-based approach where a lot of positive and negative images are used to train the classifier.
  • Positive images – These images contain the images which we want our classifier to identify.
  • Negative Images – Images of everything else, which do not contain the object we want to detect.

(Know more here ↗).

Haar-cascade Detection in Python(OpenCV)

• OpenCV comes as a detector which uses Haar Cascade.
• In face detection, we use the Haarcascade Frontal Face which is in xml format.

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Let's jump into the code

What are the requirements you need to have before running the code (all latest version)

--- python
--- opencv-python
--- haarcascade_frontalface_default.xml

Download the haar cascade files here ↗

First we need to load the required XML classifiers. Then load our input image (or video) in grayscale mode.

import numpy as np
#importing the OpenCV Lib
import cv2

#Loading the haarcascade front face file
faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

#Reading the face image
img = cv2.imread('testFace.jpg')

#Converting the image into grayscale image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Now we find the faces in the image. If faces are found, it returns the positions of detected faces as Rect(x,y,w,h).

#Detecting the face
face = faceCascade.detectMultiScale(gray, 1.1,4)

#Draw rectangle around the face
for(x,y,w,h) in face:
    cv2.rectangle(img, (x,y),(x+w, y+h), (255,0,0),2)

Now we will show the output

#Display the result with face detected
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()