Face recognition_ recognition

Github Open source face recognition project face_recognition,face_recognition Is a powerful 、 Simple 、 Open source project of face recognition

install

pip install face_recognition

Import face_recognition modular

import  face_recognition

load_image_file Load image

call face_recognition.load_image_file() Read image . The output image is rgb Format （opencv Medium is bgr Format ）

import face_recognition
image = face_recognition.load_image_file("../datas/obama.jpg")

face_locations Face detection

It can locate the face position information recognized in all images , The return value is in list form , Each line in the list is the location information of a face , Include [top, right, bottom, left]

import face_recognition
import  cv2
# Load the image file 
image = face_recognition.load_image_file("../datas/hz.png")
image2 = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
cv2.imshow("src",image2)

face_locations = face_recognition.face_locations(image)
#  Loop through all the faces found 
for face_location in face_locations:
        #  Print the position information of each face 
        top, right, bottom, left = face_location
        print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
        #  Specify the location information of the face , Then display the face picture 
        # face_image = image2[top:bottom, left:right]
        # cv2.imshow("src2", face_image)
        #  Rectangle box 
        cv2.rectangle(image2, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.imshow("dest",image2)
cv2.waitKey(0)

face_landmarks() Key points of face recognition

call face_recognition.face_landmarks(image) The key point information of human face can be recognized , Including eyes 、 nose 、 Mouth and Chin
The return value is the list containing the facial feature dictionary , Each item in the list corresponds to a face , Include nose_bridge、right_eyebrow、right_eye、chine、left_eyebrow、bottom_lip、nose_tip、top_lip、left_eye Several parts , Each part contains several feature points (x,y), All in all 68 Characteristic points .

import cv2
import face_recognition
# Load the image file 
image = face_recognition.load_image_file("images/ldh2.jpg")
image2 = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

face_landmarks_list = face_recognition.face_landmarks(image)

facial_features = [
        'chin',
        'left_eyebrow',
        'right_eyebrow',
        'nose_bridge',
        'nose_tip',
        'left_eye',
        'right_eye',
        'top_lip',
        'bottom_lip'
    ]
thickness = 2
point_size = 1
point_color = (0, 255, 0)  # BGR

for face_landmarks in face_landmarks_list:

    for facial_feature in  face_landmarks:
        points_list = face_landmarks[facial_feature]
        print("The {} in this face has the following points: {}".format(facial_feature, points_list))
        #  Draw each face feature in the image ！
        for point in points_list:
            cv2.circle(image2, point, point_size, point_color, thickness)

cv2.imshow("dest",image2)
cv2.waitKey(0)

face_encodings() Face coding

Get the face code of each face in each image file , Every face is a 128 Dimension vector

import face_recognition
# Load the image file 
image = face_recognition.load_image_file("images/ldh2.jpg")

face_encodings = face_recognition.face_encodings(image)
for face_encoding in face_encodings:
    print("face_encoding len = {} \nencoding:{}\n\n".format(len(face_encoding),face_encoding))

face_encoding len = 128
encoding:[-0.12857245 0.2251953 -0.05680346 -0.009356 -0.07961649 -0.01976449
-0.03006363 -0.2188953 0.18227896 -0.06380306 0.23052536 -0.00517259
-0.24484953 -0.1024491 -0.07127093 0.10542907 -0.15712184 -0.24606238
-0.04181587 0.01180026 0.09390671 0.01014723 0.03316356 0.08216273
-0.08543567 -0.27527595 -0.10761252 -0.08561324 0.09562894 -0.06388975
-0.07860459 -0.01040951 -0.21186014 -0.10162984 0.09300554 0.08929634
-0.01133628 -0.02549033 0.25640246 -0.03459882 -0.22288607 0.06690408
0.05515105 0.31292963 0.14994277 0.06461242 0.00211182 -0.18974975
0.11956067 -0.07477805 0.09382927 0.21616243 0.18040094 0.02733615
0.03574533 -0.20213988 -0.00134893 0.08189995 -0.1417242 -0.00179647
0.10290838 -0.06461908 -0.01702856 -0.11400557 0.20824584 0.04568703
-0.07567319 -0.24240926 0.12750657 -0.16503944 -0.10546687 0.14743967
-0.13195604 -0.13922986 -0.26378733 -0.00479815 0.42761648 0.13261758
-0.17136547 0.09223576 -0.04313177 -0.03386433 0.07099032 0.1085121
-0.10494502 0.06504983 -0.15515642 -0.00395807 0.2313281 -0.01442198
-0.08458654 0.14688034 -0.00217457 0.07351732 0.06130501 0.01452726
-0.10001963 0.01601498 -0.12459313 -0.06574792 0.00460516 -0.01154638
-0.00899489 0.13434561 -0.14506203 0.06423949 -0.01264461 0.03912276
-0.06142928 0.01014899 -0.07765882 -0.04298633 0.10122736 -0.29780281
0.25160897 0.09939459 0.02078611 0.08443137 0.10742732 0.06015015
-0.00302691 -0.00466454 -0.20120506 -0.10478879 0.12094288 -0.0389271
0.19183818 -0.00275789]

compare_faces() Face matching

compare_faces() The method can match two facial feature codes , Using the inner product of two vectors to measure the similarity , Confirm whether it is the same face according to the threshold
The first parameter gives a list of face codes （ Many faces ）
The second parameter gives a single face code （ A face ）
Default parameters ：tolerance=0.6, It can be changed according to your own needs ,tolerance The smaller the size, the tighter the match
The face that matches successfully returns True, Matching failed returns False, The order is consistent with the order of faces in the first parameter

matches ==face_recognition.compare_faces(known_face_encodings,face_encoding,tolerance=0.39)

import face_recognition
#  Load pictures of known faces 
known_image = face_recognition.load_image_file("images/yz1.jpg")

#  Load pictures of unknown faces 
unknown_image = face_recognition.load_image_file("images/yz2.jpg")

#  load 2 A picture of a known face 
ldh_encoding = face_recognition.face_encodings(known_image)[0]
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

#  Calculate the distance between the unknown picture and the known picture 
results = face_recognition.compare_faces([ldh_encoding], unknown_encoding,tolerance=0.39)[0]
print(results)

Reference link ：https://blog.csdn.net/weixin_42213622/article/details/108193123