使用 dlib 顶尖的深度学习人脸识别技术构建，在户外脸部检测数据库基准（Labeled Faces in the Wild benchmark）上的准确率高达 99.38%。
这也提供了一个简单的 face_recognition 命令行工具，你可以打开命令行中任意图像文件夹，进行人脸识别！
项目地址： /ageitgey/face_recognition#face-recognition
演示地址：/launch?template=face_recognition
读取图片
from PIL import Image, ImageDrawfrom IPython.display import display# The program we will be finding faces on the example belowpil_im = Image.open('dd.png')display(pil_im)
训练
import face_recognitionimport numpy as npfrom PIL import Image, ImageDrawfrom IPython.display import display# This is an example of running face recognition on a single image# and drawing a box around each person that was identified.# Load a sample picture and learn how to recognize it.obama_image = face_recognition.load_image_file("obama.jpg")obama_face_encoding = face_recognition.face_encodings(obama_image)[0]# Load a second sample picture and learn how to recognize it.biden_image = face_recognition.load_image_file("biden.jpg")biden_face_encoding = face_recognition.face_encodings(biden_image)[0]lyj_image = face_recognition.load_image_file("lyj.jpg")lyj_face_encoding = face_recognition.face_encodings(lyj_image)[0]# Create arrays of known face encodings and their namesknown_face_encodings = [obama_face_encoding,biden_face_encoding,lyj_face_encoding]known_face_names = ["Barack Obama","Joe Biden","lyj"]print('Learned encoding for', len(known_face_encodings), 'images.')
识别
# Load an image with an unknown faceunknown_image = face_recognition.load_image_file("22.jpg")# Find all the faces and face encodings in the unknown imageface_locations = face_recognition.face_locations(unknown_image)face_encodings = face_recognition.face_encodings(unknown_image, face_locations)# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library# See http://pillow.readthedocs.io/ for more about PIL/Pillowpil_image = Image.fromarray(unknown_image)# Create a Pillow ImageDraw Draw instance to draw withdraw = ImageDraw.Draw(pil_image)# Loop through each face found in the unknown imagefor (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):# See if the face is a match for the known face(s)matches = pare_faces(known_face_encodings, face_encoding)name = "Unknown"# Or instead, use the known face with the smallest distance to the new faceface_distances = face_recognition.face_distance(known_face_encodings, face_encoding)best_match_index = np.argmin(face_distances)if matches[best_match_index]:name = known_face_names[best_match_index]# Draw a box around the face using the Pillow moduledraw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))# Draw a label with a name below the facetext_width, text_height = draw.textsize(name)draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))# Remove the drawing library from memory as per the Pillow docsdel draw# Display the resulting imagedisplay(pil_image)
