Face Recognition with Python – Source Code Included

Face Recognition with Python

With the development of deep learning technology, facial recognition has become one of the most popular topics in the field of computer science. Facial recognition can be used for various applications such as authentication, emotion detection, and people tracking. In this article, we will discuss how to use Python for facial recognition using the OpenCV library.

What is OpenCV?

OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision. It is used in a wide variety of applications, including facial recognition. OpenCV is the most popular library for computer vision.

Python and OpenCV

Python is a powerful, high-level, versatile, and easy-to-use programming language. It is an interpreted language, which means it doesn’t need to be compiled and can be executed directly. Because of its simplicity and readability, it is often used in data science projects.

OpenCV and Python can be used together to create powerful, computer vision projects. OpenCV provides all the necessary functions and algorithms that are needed to perform facial recognition. Python is used to extend functionality beyond what OpenCV can do by itself.

Face Recognition using Python and OpenCV

In this section, we will show you how to use Python and OpenCV to perform facial recognition. We will also provide a sample code for face recognition using Python and OpenCV.

Step 1: Import Libraries

First, we need to import the libraries that we will be using. We will be using the OpenCV library for computer vision tasks, including face recognition. We will also need some additional libraries like NumPy and Matplotlib.

import cv2 
import numpy as np 
import matplotlib.pyplot as plt 

Step 2: Load Image

Next, we need to load the image that we will use for facial recognition. We need to specify the path of the image file.

img = cv2.imread('./image.jpg') 

Step 3: Convert Image to Grayscale

Next, we need to convert the image to grayscale. This is necessary because facial recognition works better with grayscale images.

gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

Step 4: Detect Faces

Now, we need to detect faces in the image. We can use the OpenCV function “detectMultiScale” to detect faces from the image. This function returns a list of rectangles where each rectangle contains the coordinates of the detected face.

faces = cv2.CascadeClassifier('haarcascade_frontalface_default.xml').detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5) 

Step 5: Draw Rectangles Around Detected Faces

Next, we need to draw rectangles around the detected faces. We can use the OpenCV function “rectangle” to draw a rectangle around each face. We can also add a label to each face.

for (x,y,w,h) in faces: 
  cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2) 
  cv2.putText(img, 'Name', (x,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2) 

Step 6: Save Image

Finally, we can save the image with the detected faces. We can use the OpenCV function “imwrite” to save the image.

cv2.imwrite('output.jpg', img) 

Conclusion

In this article, we discussed how to use Python and OpenCV for facial recognition. We provided a sample code to perform facial recognition using Python and OpenCV. We hope you found this article helpful and can now use Python and OpenCV for facial recognition applications.