Face Recognition using python

OpenCV:

OpenCV is the most popular library for computer vision. Originally written in C/C++, it now provides bindings for Python.

OpenCV uses machine learning algorithms to search for faces within a picture. Because faces are so complicated, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns and features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve.

Steps:

1. Download Python 2.7.x version, numpy and Opencv 2.7.x version.Check if your Windows either 32 bit or 64 bit is compatible and install accordingly.
2. Make sure that numpy is running in your python then try to install opencv.
3. Place the haarcascade_frontalface_default.xml files in the Same folder

Coding:

import cv2

import sys

# Get user supplied values

imagePath = sys.argv[1]

cascPath = "haarcascade_frontalface_default.xml"

# Create the haar cascade

faceCascade = cv2.CascadeClassifier(cascPath)

# Read the image

image = cv2.imread(imagePath)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the image

faces = faceCascade.detectMultiScale(

    gray,

    scaleFactor=1.1,

    minNeighbors=5,

    minSize=(30, 30)

    #flags = cv2.CV_HAAR_SCALE_IMAGE

)

print("Found {0} faces!".format(len(faces)))

# Draw a rectangle around the faces

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

    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

cv2.imshow("Faces found", image)

cv2.waitKey(0)