introduction

Face Detection is the first and essential step for face recognition, and it is used to detect faces in the images. It is a part of object detection and can use in many areas such as security, bio-metrics, law enforcement, entertainment, personal safety.

It is used to detect faces in real time for surveillance and tracking of person or objects. It is widely used in cameras to identify multiple appearances in the frame Ex- Mobile cameras and DSLR’s. Facebook is also using face detection algorithm to detect faces in the images and recognise them.

How the Face Detection Works......

There are many techniques to detect faces, with the help of these techniques, we can identify faces with higher accuracy. These techniques have an almost same procedure for Face Detection such as OpenCV, Neural Networks, Matlab, etc. The face detection work as to detect multiple faces in an image. Here we work on OpenCV for Face Detection, and there are some steps that how face detection operates, which are as follows-

Firstly the image is imported by providing the location of the image. Then the picture is transformed from RGB to Grayscale because it is easy to detect faces in the grayscale.

After that, the image manipulation used, in which the resizing, cropping, blurring and sharpening of the images done if needed. The next step is image segmentation, which is used for contour detection or segments the multiple objects in a single image so that the classifier can quickly detect the objects and faces in the picture.

The next step is to use Haar-Like features algorithm, which is proposed by Voila and Jones for face detection. This algorithm used for finding the location of the human faces in a frame or image. All human faces shares some universal properties of the human face like the eyes region is darker than its Neighbour pixels and nose region is brighter than eye region.

Face Detector in Real-Time (Webcam).

import libraries

import cv2
import numpy as np


import classifier for face and eye detection

face_classifier = cv2.CascadeClassifier(‘Haarcascades/haarcascade_frontalface_default.xml’)

Import Classifier for Face and Eye Detection

face_classifier = cv2.CascadeClassifier(‘Haarcascades/haarcascade_frontalface_default.xml’)
eye_classifier = cv2.CascadeClassifier (‘Haarcascades/haarcascade_eye.xml’)
def face_detector (img, size=0.5):

Convert Image to Grayscale

gray = cv2.cvtColor (img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale (gray, 1.3, 5)
If faces is ():
return img

Given coordinates to detect face and eyes location from ROI

for (x, y, w, h) in faces
x = x — 100
w = w + 100
y = y — 100
h = h + 100
cv2.rectangle (img, (x, y), (x+w, y+h), (255, 0, 0), 2)
roi_gray = gray[y: y+h, x: x+w]
roi_color = img[y: y+h, x: x+w]
eyes = eye_classifier.detectMultiScale (roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,0,255),2)
roi_color = cv2.flip (roi_color, 1)
return roi_color

Webcam setup for Face Detection

cap = cv2.VideoCapture (0)
while True:
ret, frame = cap.read ()
cv2.imshow (‘Our Face Extractor’, face_detector (frame))
if cv2.waitKey (1) == 13: #13 is the Enter Key
break

When everything done, release the capture

cap.release ()
cv2.destroyAllWindows ()