1.Face Detection

 The most basic task on Face Recognition is of course, “Face Detecting”. Before anything, you must “capture” a face (Phase 1) in order to recognize it, when compared with a new face captured on future (Phase 3).

The most common way to detect a face (or any objects), is using the “Haar Cascade classifier”

Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images.

Here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. The good news is that OpenCV comes with a trainer as well as a detector. If you want to train your own classifier for any object like car, planes etc. you can use OpenCV to create one. Its full details are given here: Cascade Classifier Training.

If you do not want to create your own classifier, OpenCV already contains many pre-trained classifiers for face, eyes, smile, etc. Those XML files can be download from haarcascades directory.

Enough theory, let’s create a face detector with OpenCV!

Download the file: faceDetection.py from my GitHub.

import numpy as np
import cv2faceCascade = cv2.CascadeClassifier('Cascades/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
cap.set(3,640) # set Width
cap.set(4,480) # set Heightwhile True:
    ret, img = cap.read()
    img = cv2.flip(img, -1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(
        gray,     
        scaleFactor=1.2,
        minNeighbors=5,     
        minSize=(20, 20)
    )
    for (x,y,w,h) in faces:
        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]  
    cv2.imshow('video',img)
    k = cv2.waitKey(30) & 0xff
    if k == 27: # press 'ESC' to quit
        breakcap.release()
cv2.destroyAllWindows()

Believe it or not, the above few lines of code are all you need to detect a face, using Python and OpenCV.

When you compare with the last code used to test the camera, you will realize that few parts were added to it. Note the line below:

faceCascade = cv2.CascadeClassifier('Cascades/haarcascade_frontalface_default.xml')

This is the line that loads the “classifier” (that must be in a directory named “Cascades/”, under your project directory).

Then, we will set our camera and inside the loop, load our input video in grayscale mode (same we saw before).

Now we must call our classifier function, passing it some very important parameters, as scale factor, number of neighbors and minimum size of the detected face.

faces = faceCascade.detectMultiScale(
        gray,     
        scaleFactor=1.2,
        minNeighbors=5,     
        minSize=(20, 20)
        )

Where,

• gray is the input grayscale image.
• scaleFactor is the parameter specifying how much the image size is reduced at each image scale. It is used to create the scale pyramid.
• minNeighbors is a parameter specifying how many neighbors each candidate rectangle should have, to retain it. A higher number gives lower false positives.
• minSize is the minimum rectangle size to be considered a face.

The function will detect faces on the image. Next, we must “mark” the faces in the image, using, for example, a blue rectangle. This is done with this portion of the code:

for (x,y,w,h) in faces:
    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]

If faces are found, it returns the positions of detected faces as a rectangle with the left up corner (x,y) and having “w” as its Width and “h” as its Height ==> (x,y,w,h). Please see the picture.

Once we get these locations, we can create an “ROI” (drawn rectangle) for the face and present the result with imshow() function.

python faceDetection.py