超越Dlib！81个特征点覆盖全脸，面部特征点检测更精准(附代码)

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超越Dlib！81个特征点覆盖全脸，面部特征点检测更精准(附代码)

2019 年 3 月 18 日 新智元

新智元原创

来源：Reddit、GitHub

编辑: 金磊

【新智元导读】人脸特征点检测是人脸检测过程中的一个重要环节。以往我们采用的方法是OpenCV或者Dlib，虽然Dlib优于OpenCV，但是检测出的68个点并没有覆盖额头区域。Reddit一位网友便在此基础上做了进一步研究，能够检测出81个面部特征点，使得准确度有所提高。

或许，这就是你需要的人脸特征点检测方法。

人脸特征点检测(Facial landmark detection)是人脸检测过程中的一个重要环节。是在人脸检测的基础上进行的，对人脸上的特征点例如嘴角、眼角等进行定位。

近日，Reddit一位网友po出一个帖子，表示想与社区同胞们分享自己的一点研究成果：

其主要的工作就是在人脸检测Dlib库68个特征点的基础上，增加了13个特征点(共81个) ，使得头部检测和图像操作更加精确。

现在来看一下demo：

demo视频链接：

https://www.youtube.com/watch?v=mDJrASIB1T0

81个特征点，人脸特征点检测更加精准

以往我们在做人脸特征点检测的时候，通常会用OpenCV来进行操作。

但自从人脸检测Dlib库问世，网友们纷纷表示：好用！Dlib≥OpenCV！Dlib具有更多的人脸识别模型，可以检测脸部68甚至更多的特征点。

我们来看一下Dlib的效果：

Dlib人脸特征点检测效果图

那么这68个特征点又是如何分布的呢？请看下面这张“面相图”：

人脸68个特征点分布

但无论是效果图和“面相图”，我们都可以发现在额头区域是没有分布特征点的。

于是，网友便提出了一个特征点能够覆盖额头区域的模型。

该模型是一个自定义形状预测模型，在经过训练后，可以找到任何给定图像中的81个面部特征点。

它的训练方法类似于Dlib的68个面部特征点形状预测器。只是在原有的68个特征点的基础上，在额头区域增加了13个点。这就使得头部的检测，以及用于需要沿着头部顶部的点的图像操作更加精准。

81个特征点效果图

这13个额外的特征点提取的方法，是根据该博主之前的工作完成的。

GitHub地址：

https://github.com/codeniko/eos

该博主继续使用Surrey Face Model，并记下了他认为适合他工作的13个点，并做了一些细节的修改。

当然，博主还慷慨的分享了训练的代码：

1#!/usr/bin/python
2# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
3#
4#   This example program shows how to use dlib's implementation of the paper:
5#   One Millisecond Face Alignment with an Ensemble of Regression Trees by
6#   Vahid Kazemi and Josephine Sullivan, CVPR 2014
7#
8#   In particular, we will train a face landmarking model based on a small
9#   dataset and then evaluate it.  If you want to visualize the output of the
10#   trained model on some images then you can run the
11#   face_landmark_detection.py example program with predictor.dat as the input
12#   model.
13#
14#   It should also be noted that this kind of model, while often used for face
15#   landmarking, is quite general and can be used for a variety of shape
16#   prediction tasks.  But here we demonstrate it only on a simple face
17#   landmarking task.
18#
19# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
20#   You can install dlib using the command:
21#       pip install dlib
22#
23#   Alternatively, if you want to compile dlib yourself then go into the dlib
24#   root folder and run:
25#       python setup.py install
26#
27#   Compiling dlib should work on any operating system so long as you have
28#   CMake installed.  On Ubuntu, this can be done easily by running the
29#   command:
30#       sudo apt-get install cmake
31#
32#   Also note that this example requires Numpy which can be installed
33#   via the command:
34#       pip install numpy
35
36import os
37import sys
38import glob
39
40import dlib
41
42# In this example we are going to train a face detector based on the small
43# faces dataset in the examples/faces directory.  This means you need to supply
44# the path to this faces folder as a command line argument so we will know
45# where it is.
46if len(sys.argv) != 2:
47    print(
48        "Give the path to the examples/faces directory as the argument to this "
49        "program. For example, if you are in the python_examples folder then "
50        "execute this program by running:\n"
51        "    ./train_shape_predictor.py ../examples/faces")
52    exit()
53faces_folder = sys.argv[1]
54
55options = dlib.shape_predictor_training_options()
56# Now make the object responsible for training the model.
57# This algorithm has a bunch of parameters you can mess with.  The
58# documentation for the shape_predictor_trainer explains all of them.
59# You should also read Kazemi's paper which explains all the parameters
60# in great detail.  However, here I'm just setting three of them
61# differently than their default values.  I'm doing this because we
62# have a very small dataset.  In particular, setting the oversampling
63# to a high amount (300) effectively boosts the training set size, so
64# that helps this example.
65options.oversampling_amount = 300
66# I'm also reducing the capacity of the model by explicitly increasing
67# the regularization (making nu smaller) and by using trees with
68# smaller depths.
69options.nu = 0.05
70options.tree_depth = 2
71options.be_verbose = True
72
73# dlib.train_shape_predictor() does the actual training.  It will save the
74# final predictor to predictor.dat.  The input is an XML file that lists the
75# images in the training dataset and also contains the positions of the face
76# parts.
77training_xml_path = os.path.join(faces_folder, "training_with_face_landmarks.xml")
78dlib.train_shape_predictor(training_xml_path, "predictor.dat", options)
79
80# Now that we have a model we can test it.  dlib.test_shape_predictor()
81# measures the average distance between a face landmark output by the
82# shape_predictor and where it should be according to the truth data.
83print("\nTraining accuracy: {}".format(
84    dlib.test_shape_predictor(training_xml_path, "predictor.dat")))
85# The real test is to see how well it does on data it wasn't trained on.  We
86# trained it on a very small dataset so the accuracy is not extremely high, but
87# it's still doing quite good.  Moreover, if you train it on one of the large
88# face landmarking datasets you will obtain state-of-the-art results, as shown
89# in the Kazemi paper.
90testing_xml_path = os.path.join(faces_folder, "testing_with_face_landmarks.xml")
91print("Testing accuracy: {}".format(
92    dlib.test_shape_predictor(testing_xml_path, "predictor.dat")))
93
94# Now let's use it as you would in a normal application.  First we will load it
95# from disk. We also need to load a face detector to provide the initial
96# estimate of the facial location.
97predictor = dlib.shape_predictor("predictor.dat")
98detector = dlib.get_frontal_face_detector()
99
100# Now let's run the detector and shape_predictor over the images in the faces
101# folder and display the results.
102print("Showing detections and predictions on the images in the faces folder...")
103win = dlib.image_window()
104for f in glob.glob(os.path.join(faces_folder, "*.jpg")):
105    print("Processing file: {}".format(f))
106    img = dlib.load_rgb_image(f)
107
108    win.clear_overlay()
109    win.set_image(img)
110
111    # Ask the detector to find the bounding boxes of each face. The 1 in the
112    # second argument indicates that we should upsample the image 1 time. This
113    # will make everything bigger and allow us to detect more faces.
114    dets = detector(img, 1)
115    print("Number of faces detected: {}".format(len(dets)))
116    for k, d in enumerate(dets):
117        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
118            k, d.left(), d.top(), d.right(), d.bottom()))
119        # Get the landmarks/parts for the face in box d.
120        shape = predictor(img, d)
121        print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
122                                                  shape.part(1)))
123        # Draw the face landmarks on the screen.
124        win.add_overlay(shape)
125
126    win.add_overlay(dets)
127    dlib.hit_enter_to_continue()