Face_Recognition简单使用

API 接口文档: https://face-recognition.readthedocs.io

定位图片中所有人脸

1 import face_recognition
2 image = face_recognition.load_image_file("your_file.jpg")
3 face_locations = face_recognition.face_locations(image)
4 #face_locations 以列表形式返回图片中的所有人脸

定位所有人脸

 1 import face_recognition
 2 from PIL import Image
 3 
 4 image = face_recognition.load_image_file("your_file.jpg")
 5 face_locations = face_recognition.face_locations(image)
 6 #face_locations 以列表形式返回图片中的所有人脸
 7 
 8 top, right, bottom, left = face_locations[0]
 9 face_image = image[top:bottom, left:right]
10 pil_image = Image.fromarray(face_image)
11 pil_image.save(fp=“your_new_file_path.jpg”)

将定位到的人脸切割另存

你也可以使用深度学习模型达到更加精准的人脸定位。

注意：这种方法需要GPU加速（通过英伟达显卡的CUDA库驱动），你在编译安装dlib的时候也需要开启CUDA支持。

import face_recognition

image = face_recognition.load_image_file("my_picture.jpg")
face_locations = face_recognition.face_locations(image, model="cnn")
# face_locations is now an array listing the co-ordinates of each face!

看案例：使用卷积神经网络深度学习模型定位拜登的脸

如果你有很多图片需要识别，同时又有GPU，那么你可以参考这个例子：案例：使用卷积神经网络深度学习模型批量识别图片中的人脸.

识别单张图片中人脸的关键点

import face_recognition

image = face_recognition.load_image_file("my_picture.jpg")
face_landmarks_list = face_recognition.face_landmarks(image)

# face_landmarks_list is now an array with the locations of each facial feature in each face.
# face_landmarks_list[0]['left_eye'] would be the location and outline of the first person's left eye.

看这个案例案例：提取奥巴马和拜登的面部关键点

import face_recognition

image = face_recognition.load_image_file("my_picture.jpg")
face_landmarks_list = face_recognition.face_landmarks(image)face_landmarks源码中五官对应的点

"chin": points[0:17],"left_eyebrow": points[17:22],"right_eyebrow": points[22:27],"nose_bridge": points[27:31],"nose_tip": points[31:36],"left_eye": points[36:42],"right_eye": points[42:48],"top_lip": points[48:55] + [points[64]] + [points[63]] + [points[62]] + [points[61]] + [points[60]],"bottom_lip": points[54:60] + [points[48]] + [points[60]] + [points[67]] + [points[66]] + [points[65]] + [points[64]

top_lip --上嘴唇   
 
    face_landmarks_list[0]['top_lip'][0]   --points[48]

    face_landmarks_list[0]['top_lip'][1]   --points[49]

    face_landmarks_list[0]['top_lip'][2]   --points[50]

    face_landmarks_list[0]['top_lip'][3]   --points[51]

    face_landmarks_list3[0]['top_lip'][4]   --points[52]

    face_landmarks_list3[0]['top_lip'][5]   --points[53]

    face_landmarks_list3[0]['top_lip'][6]  --points[54]

    face_landmarks_list3[0]['top_lip'][7]  --points[64]

    face_landmarks_list3[0]['top_lip'][8]  --points[63]

    face_landmarks_list3[0]['top_lip'][9]  --points[62]

    face_landmarks_list3[0]['top_lip'][10]  --points[61]

    face_landmarks_list3[0]['top_lip'][11]  --points[60]

        
bottom_lip --下嘴唇

    face_landmarks_list[0]['bottom_lip'][0]  --points[54]

    face_landmarks_list[0]['bottom_lip'][1]  --points[55]

    face_landmarks_list[0]['bottom_lip'][2]  --points[56]

    face_landmarks_list[0]['bottom_lip'][3]  --points[57]

    face_landmarks_list[0]['bottom_lip'][4]  --points[58]

    face_landmarks_list[0]['bottom_lip'][5]  --points[59]

    face_landmarks_list[0]['bottom_lip'][6]  --points[48]

    face_landmarks_list[0]['bottom_lip'][7]  --points[60]

    face_landmarks_list[0]['bottom_lip'][8]  --points[67]

    face_landmarks_list[0]['bottom_lip'][9]  --points[66]

    face_landmarks_list[0]['bottom_lip'][10]  --points[65]

    face_landmarks_list[0]['bottom_lip'][11]  --points[64]

上图是采用奥巴马一张照片识别五官后上嘴唇以及下嘴唇对应点以及对应位置的标注，左眼1-6是返回列表1-6的数值对应的像素位置。

识别图片人物

import face_recognition

picture_of_me = face_recognition.load_image_file("me.jpg")
my_face_encoding = face_recognition.face_encodings(picture_of_me)[0]
# my_face_encoding now contains a universal 'encoding' of my facial features that can be compared to any other picture of a face!

unknown_picture = face_recognition.load_image_file("unknown.jpg")
unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0]

# Now we can see the two face encodings are of the same person with `compare_faces`!

results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding)

if results[0] == True:
    print("It's a picture of me!")
else:
    print("It's not a picture of me!")

看这个案例案例：是奥巴马还是拜登？

调整人脸识别的容错率和敏感度

如果一张脸识别出不止一个结果，那么这意味着他和其他人长的太像了（Face_Recognition对于小孩和亚洲人的人脸识别准确率有待提升）。你可以把容错率调低一些，使识别结果更加严格。

通过传入参数 tolerance 来实现这个功能，默认的容错率是0.6，容错率越低，识别越严格准确。

如果想要更准确的识别，可以在计算编码时设定要重新采样的次数，face_encodings传入 num_jitters 来实现，默认0，范围为0-100，越高越准确，但速度越慢，（100就会慢100倍）

import face_recognition

picture_of_me = face_recognition.load_image_file("me.jpg")
my_face_encoding = face_recognition.face_encodings(picture_of_me,num_jitters=100)[0]

unknown_picture = face_recognition.load_image_file("unknown.jpg")
unknown_face_encoding = face_recognition.face_encodings(unknown_picture,num_jitters=100)[0]
# 由于对亚洲人识别率不高tolerance一般设置在0.3-0.38之间可满足大部分需求results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding,tolerance=0.38)

Python 案例

人脸定位

人脸关键点识别

- 案例：提取奥巴马和拜登的面部关键点
- 案例：给美国副总统拜登涂美妆

人脸识别

参考：https://github.com/ageitgey/face_recognition#face-recognition

来源：https://www.cnblogs.com/layerluo/p/11413000.html

标签

人脸识别算法