史上最简单的人脸识别项目登上GitHub趋势榜

导读：近日，一个名为 face_recognition 的人脸识别项目登上了 GitHub Trending 趋势榜，赚足了眼球。自开源至截稿，此项目在 Github 上的 Star 数已达 26500，Fork 数也达到了 7117。本文主要介绍了该项目的使用说明和使用方法，便于国内的开发者们进行研究学习。

face_recognition 宣称是史上最强大，最简单的人脸识别项目。据悉，该项目由软件工程开发师和咨询师 Adam Geitgey 开发，其强大之处在于不仅基于业内领先的 C++ 开源库 dlib 中的深度学习模型，采用的人脸数据集也是由美国麻省大学安姆斯特分校制作的 Labeled Faces in the Wild，它含有从网络收集的 13,000 多张面部图像，准确率高达 99.38%。此外，项目还配备了完整的开发文档和应用案例，特别是兼容树莓派系统。简单之处在于操作者可以直接使用 Python和命令行工具提取、识别、操作人脸。

目前，该项目的说明已有中文翻译版，本文已获得译者授权（GitHub ID：TommyZihao），引用译文对本项目进行简单介绍。

照例先奉上 GitHub 项目链接：

https://github.com/ageitgey/face_recognitio

特性

1. 找到并定位图片中的所有人脸：

import face_recognition	
image = face_recognition.load_image_file("your_file.jpg")	
face_locations = face_recognition.face_locations(image)

2. 识别人脸关键点（包括眼睛、鼻子、嘴和下巴）

import face_recognition	
image = face_recognition.load_image_file("your_file.jpg")	
face_landmarks_list = face_recognition.face_landmarks(image)

3. 识别图片中的人是谁

import face_recognition	
known_image = face_recognition.load_image_file("biden.jpg")	
unknown_image = face_recognition.load_image_file("unknown.jpg")	
biden_encoding = face_recognition.face_encodings(known_image)[0]	
results = face_recognition.compare_faces([biden_encoding], unknown_encoding)

https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py

安装

环境配置

不同操作系统的安装方法

https://gist.github.com/ageitgey/629d75c1baac34dfa5ca2a1928a7aeaf

pip3 install face_recognition

https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b

export CC=/usr/local/bin/gcc	
export CXX=/usr/local/bin/g++

https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65

https://github.com/ageitgey/face_recognition/issues/175#issue-257710508

https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b

使用命令行工具
本项目安装完毕后，有两种命令行工具可供使用：

$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/	/unknown_pictures/unknown.jpg,Barack Obama	
/face_recognition_test/unknown_pictures/unknown.jpg,unknown_person

$ face_detection  ./folder_with_pictures/	examples/image1.jpg,65,215,169,112	
examples/image2.jpg,62,394,211,244	
examples/image2.jpg,95,941,244,792

$ face_recognition --tolerance 0.54 ./pictures_of_people_i_know/ ./unknown_pictures/	/unknown_pictures/unknown.jpg,Barack Obama	
/face_recognition_test/unknown_pictures/unknown.jpg,unknown_person

$ face_recognition --show-distance true ./pictures_of_people_i_know/ ./unknown_pictures/	/unknown_pictures/unknown.jpg,Barack Obama,0.378542298956785	
/face_recognition_test/unknown_pictures/unknown.jpg,unknown_person,None

$ face_recognition --cpus 4 ./pictures_of_people_i_know/ ./unknown_pictures/

$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2	Barack Obama	
unknown_person

使用Python

https://face-recognition.readthedocs.io

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

https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py

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

参考案例：

https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py

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

https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py 

https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_batches.py

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

https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py

 更多案例：

https://github.com/ageitgey/face_recognition/tree/master/examples

https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py

https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py

https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_batches.py

https://github.com/ageitgey/face_recognition/blob/master/examples/blur_faces_on_webcam.py

https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py

https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py

https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py

https://github.com/ageitgey/face_recognition/blob/master/examples/identify_and_draw_boxes_on_faces.py

https://github.com/ageitgey/face_recognition/blob/master/examples/face_distance.py

https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam.py

关于 face_recognition的文章和教程

人脸识别的原理

https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78

警告说明

把本项目部署在云服务器上 (Heroku, AWS等)

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