Face recognition example in Python

With the continuous development of computer technology, artificial intelligence technology has received more and more attention and application, among which face recognition technology is the most popular direction. As one of the most popular programming languages ​​at present, Python is increasingly used in face recognition. This article will introduce face recognition examples in Python.

1. OpenCV

OpenCV is an open source computer vision library that provides a variety of algorithm-based image processing and computer vision methods. In Python, we can use OpenCV to implement face recognition.

First you need to import the OpenCV module:

import cv2

Then, use the CascadeClassifier function provided by OpenCV for face recognition:

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Among them, haarcascade_frontalface_default.xml is a pre-trained model provided in OpenCV for detecting faces.

Next, we need to read the image and process it:

img = cv2.imread('test.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Among them, test.jpg is the image to be processed, and the cvtColor function will The image is converted to grayscale.

Finally, face recognition is performed on the processed image:

faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

Among them, the detectMultiScale function is used to detect the face in the image and returns the face The coordinates and size of the box. Finally, we use the rectangle function to draw the face frame on the original image.

2. face_recognition

face_recognition is a face recognition library based on dlib and Python. It uses deep learning methods for face recognition and has high accuracy and robustness.

You need to install the face_recognition library before use:

pip install face_recognition

Then, we need to read the image and process it:

import face_recognition
import matplotlib.pyplot as plt

image = face_recognition.load_image_file("test.jpg")
face_locations = face_recognition.face_locations(image)
plt.imshow(image)

Among them, face_recognition.load_image_file The function is used to read pictures, and the face_recognition.face_locations function is used to detect face locations in pictures.

Finally, we can mark the position of the face in the image:

import numpy as np
import cv2

for face_location in face_locations:
    top, right, bottom, left = face_location
    cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
plt.imshow(image)

Among them, the cv2.rectangle function is used to mark the rectangular frame on the original image, indicating The position of the human face.

Conclusion

The application range of face recognition technology is becoming more and more extensive. Python, as one of the most popular programming languages ​​at present, also has excellent performance in the field of face recognition. The two examples introduced above, through the application of OpenCV and face_recognition library, allow us to realize the face recognition function more conveniently and quickly.

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