Skip to content
Related Articles

Related Articles

Face Detection using Python and OpenCV with webcam
  • Difficulty Level : Medium
  • Last Updated : 06 Nov, 2018

OpenCV is a Library which is used to carry out image processing using programming languages like python. This project utilizes OpenCV Library to make a Real-Time Face Detection using your webcam as a primary camera.

Following are the requirements for it:-

  1. Python 2.7
  2. OpenCV
  3. Numpy
  4. Haar Cascade Frontal face classifiers

Approach/Algorithms used:

  1. This project uses LBPH (Local Binary Patterns Histograms) Algorithm to detect faces. It labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number.
  2. LBPH uses 4 parameters :
    (i) Radius: the radius is used to build the circular local binary pattern and represents the radius around the
    central pixel.
    (ii) Neighbors : the number of sample points to build the circular local binary pattern.
    (iii) Grid X : the number of cells in the horizontal direction.
    (iv) Grid Y : the number of cells in the vertical direction.
  3. The model built is trained with the faces with tag given to them, and later on, the machine is given a test data and machine decides the correct label for it.

How to use :

  1. Create a directory in your pc and name it (say project)
  2. Create two python files named and, copy the first source code and second source code in it respectively.
  3. Copy haarcascade_frontalface_default.xml to the project directory, you can get it in opencv or from
  4. You are ready to now run the following codes.

# Creating database
# It captures images and stores them in datasets 
# folder under the folder name of sub_data
import cv2, sys, numpy, os
haar_file = 'haarcascade_frontalface_default.xml'
# All the faces data will be
#  present this folder
datasets = 'datasets'  
# These are sub data sets of folder, 
# for my faces I've used my name you can 
# change the label here
sub_data = 'vivek'     
path = os.path.join(datasets, sub_data)
if not os.path.isdir(path):
# defining the size of images 
(width, height) = (130, 100)    
#'0' is used for my webcam, 
# if you've any other camera
#  attached use '1' like this
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0
# The program loops until it has 30 images of the face.
count = 1
while count < 30
    (_, im) =
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 4)
    for (x, y, w, h) in faces:
        cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
        face = gray[y:y + h, x:x + w]
        face_resize = cv2.resize(face, (width, height))
        cv2.imwrite('% s/% s.png' % (path, count), face_resize)
    count += 1
    cv2.imshow('OpenCV', im)
    key = cv2.waitKey(10)
    if key == 27:

Following code should be run after the model has been trained for the faces :

# It helps in identifying the faces
import cv2, sys, numpy, os
size = 4
haar_file = 'haarcascade_frontalface_default.xml'
datasets = 'datasets'
# Part 1: Create fisherRecognizer
print('Recognizing Face Please Be in sufficient Lights...')
# Create a list of images and a list of corresponding names
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
    for subdir in dirs:
        names[id] = subdir
        subjectpath = os.path.join(datasets, subdir)
        for filename in os.listdir(subjectpath):
            path = subjectpath + '/' + filename
            lable = id
            images.append(cv2.imread(path, 0))
        id += 1
(width, height) = (130, 100)
# Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, lables)
# Part 2: Use fisherRecognizer on camera stream
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)
while True:
    (_, im) =
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
        face = gray[y:y + h, x:x + w]
        face_resize = cv2.resize(face, (width, height))
        # Try to recognize the face
        prediction = model.predict(face_resize)
        cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
        if prediction[1]<500:
           cv2.putText(im, '% s - %.0f' % 
(names[prediction[0]], prediction[1]), (x-10, y-10), 
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
          cv2.putText(im, 'not recognized'
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
    cv2.imshow('OpenCV', im)
    key = cv2.waitKey(10)
    if key == 27:

Note : Above programs will not run on online IDE.

Screenshots of the Program

It may look something different because I had integrated the above program on flask framework

Running of second program yields results similar to the below image :

face detection

face detection

Datasets Storage :



 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :