之前已经介绍过人脸识别的基础概念，以及基于opencv的实现方式，今天，我们使用dlib来提取128维的人脸嵌入，并使用k临近值方法来实现人脸识别。

人脸识别系统的实现流程与之前是一样的，只是这里我们借助了dlib和face_recognition这两个库来实现。face_recognition是对dlib库的包装，使对dlib的使用更方便。所以首先要安装这2个库。

pip3 install dlib

pip3 install face_recognition

然后，还要安装imutils库

pip3 install imutils

我们看一下项目的目录结构：

.

├── dataset

│   ├── alan_grant [22 entries exceeds filelimit, not opening dir]

│   ├── claire_dearing [53 entries exceeds filelimit, not opening dir]

│   ├── ellie_sattler [31 entries exceeds filelimit, not opening dir]

│   ├── ian_malcolm [41 entries exceeds filelimit, not opening dir]

│   ├── john_hammond [36 entries exceeds filelimit, not opening dir]

│   └── owen_grady [35 entries exceeds filelimit, not opening dir]

├── examples

│   ├── example_01.png

│   ├── example_02.png

│   └── example_03.png

├── output

│   ├── lunch_scene_output.avi

│   └── webcam_face_recognition_output.avi

├── videos

│   └── lunch_scene.mp4

├── encode_faces.py

├── encodings.pickle

├── recognize_faces_image.py

├── recognize_faces_video_file.py

├── recognize_faces_video.py

└── search_bing_api.py

10 directories, 12 files

首先，提取128维的人脸嵌入：

命令如下：

python3 encode_faces.py --dataset dataset --encodings encodings.pickle -d hog

记住：如果你的电脑内存不够大，请使用hog模型进行人脸检测，如果内存够大，可以使用cnn神经网络进行人脸检测。

看代码：

# USAGE

# python encode_faces.py --dataset dataset --encodings encodings.pickle

# import the necessary packages

from imutils import paths

import face_recognition

import argparse

import pickle

import cv2

import os

# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-i", "--dataset", required=True,

help="path to input directory of faces + images")

ap.add_argument("-e", "--encodings", required=True,

help="path to serialized db of facial encodings")

ap.add_argument("-d", "--detection-method", type=str, default="hog",

help="face detection model to use: either `hog` or `cnn`")

args = vars(ap.parse_args())

# grab the paths to the input images in our dataset

print("[INFO] quantifying faces...")

imagePaths = list(paths.list_images(args["dataset"]))

# initialize the list of known encodings and known names

knownEncodings = []

knownNames = []

# loop over the image paths

for (i, imagePath) in enumerate(imagePaths):

# extract the person name from the image path

print("[INFO] processing image {}/{}".format(i + 1,

len(imagePaths)))

name = imagePath.split(os.path.sep)[-2]

# load the input image and convert it from RGB (OpenCV ordering)

# to dlib ordering (RGB)

image = cv2.imread(imagePath)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# detect the (x, y)-coordinates of the bounding boxes

# corresponding to each face in the input image

boxes = face_recognition.face_locations(rgb,

model=args["detection_method"])

# compute the facial embedding for the face

encodings = face_recognition.face_encodings(rgb, boxes)

# loop over the encodings

for encoding in encodings:

# add each encoding + name to our set of known names and

# encodings

knownEncodings.append(encoding)

knownNames.append(name)

# dump the facial encodings + names to disk

print("[INFO] serializing encodings...")

data = {"encodings": knownEncodings, "names": knownNames}

f = open(args["encodings"], "wb")

f.write(pickle.dumps(data))

f.close()

输出结果是每张图片输出一个人脸的128维的向量和对于的名字，并序列化到硬盘，供后续人脸识别使用。

识别图像中的人脸：

这里使用KNN方法实现最终的人脸识别，而不是使用SVM进行训练。

命令如下：

python3 recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png

看代码：

# USAGE

# python recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png

# import the necessary packages

import face_recognition

import argparse

import pickle

import cv2

# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-e", "--encodings", required=True,

help="path to serialized db of facial encodings")

ap.add_argument("-i", "--image", required=True,

help="path to input image")

ap.add_argument("-d", "--detection-method", type=str, default="cnn",

help="face detection model to use: either `hog` or `cnn`")

args = vars(ap.parse_args())

# load the known faces and embeddings

print("[INFO] loading encodings...")

data = pickle.loads(open(args["encodings"], "rb").read())

# load the input image and convert it from BGR to RGB

image = cv2.imread(args["image"])

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# detect the (x, y)-coordinates of the bounding boxes corresponding

# to each face in the input image, then compute the facial embeddings

# for each face

print("[INFO] recognizing faces...")

boxes = face_recognition.face_locations(rgb,

model=args["detection_method"])

encodings = face_recognition.face_encodings(rgb, boxes)

# initialize the list of names for each face detected

names = []

# loop over the facial embeddings

for encoding in encodings:

# attempt to match each face in the input image to our known

# encodings

matches = face_recognition.compare_faces(data["encodings"],

encoding)

name = "Unknown"

# check to see if we have found a match

if True in matches:

# find the indexes of all matched faces then initialize a

# dictionary to count the total number of times each face

# was matched

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

name = data["names"][i]

counts[name] = counts.get(name, 0) + 1

# determine the recognized face with the largest number of

# votes (note: in the event of an unlikely tie Python will

# select first entry in the dictionary)

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((top, right, bottom, left), name) in zip(boxes, names):

# draw the predicted face name on the image

cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)

y = top - 15 if top - 15 > 15 else top + 15

cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

# show the output image

cv2.imshow("Image", image)

cv2.waitKey(0)

实际效果如下：

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