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Detect and recognize the faces from camera / 调用摄像头进行人脸识别,支持多张人脸同时识别

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Face recognition from camera with Dlib


Detect and recognize single/multi-faces from camera;


. 摄像头人脸录入 / Face register

.. image:: introduction/face_register.png :align: center

请不要离摄像头过近,人脸超出摄像头范围时会有 "OUT OF RANGE" 提醒 / Please do not be too close to the camera, or you can't save faces with "OUT OF RANGE" warning;

.. image:: introduction/faceregisterwarning.png :align: center

. 提取特征建立人脸数据库 / Generate database from images captured

. 利用摄像头进行人脸识别 / Face recognizer

当单张人脸 / When single-face:

.. image:: introduction/facerecosingle.png :align: center

利用 OT 对于单张人脸追踪/ Use OT to track, which can improve FPS from 1.x to 20.x:

.. image:: introduction/facerecosingle_ot.png :align: center

当多张人脸 / When multi-faces:

.. image:: introduction/facerecomulti.png :align: center

利用 OT 来实现 / When multi-faces with OT:

.. image:: introduction/facerecomulti_ot.png :align: center

定制显示名字, 可以写中文 / Customize names:

.. image:: introduction/facerecowith_name.png :align: center

** 关于精度 / About accuracy:

  • When using a distance threshold of
    , the dlib model obtains an accuracy of
    on the standard LFW face recognition benchmark.

** 关于算法 / About algorithm

  • 基于 Residual Neural Network / 残差网络的 CNN 模型;

  • This model is a ResNet network with 29 conv layers. It's essentially a version of the ResNet-34 network from the paper Deep Residual Learning for Image Recognition by He, Zhang, Ren, and Sun with a few layers removed and the number of filters per layer reduced by half.


此项目中人脸识别的实现流程 (no OT, 每一帧都进行检测+识别) / The design of this repo:

.. image:: introduction/overview.png :align: center

实现流程(with OT, 初始帧进行检测+识别,后续帧检测+质心跟踪) / The design of this repo:

.. image:: introduction/overviewwithot.png :align: center

如果利用 OT 来跟踪,可以大大提高 FPS, 因为做识别时候需要提取特征描述子的耗时很多;


. 安装依赖库 / Install some python packages if needed

.. code-block:: bash

  pip3 install opencv-python
  pip3 install scikit-image
  pip3 install dlib

. 下载源码 / Download zip from website or via GitHub Desktop in windows, or git clone repo in Ubuntu

.. code-block:: bash

  git clone

. 进行人脸信息采集录入 / Register faces

.. code-block:: bash


. 提取所有录入人脸数据存入 "featuresall.csv" / Features extraction and save into "featuresall.csv"

.. code-block:: bash


. 调用摄像头进行实时人脸识别 / Real-time face recognition

.. code-block:: bash


. 或者利用 OT 算法,调用摄像头进行实时人脸识别/ Real-time face recognition with OT

.. code-block:: bash


About Source Code

Repo 的 tree / 树状图:


├──              # Step 1. Face register
├──         # Step 2. Feature extraction
├──              # Step 3. Face recognizer
├──       # Step 3. Face recognizer with OT for single person
├──        # Step 3. Face recognizer with OT for multi people
├──        # Face descriptor computation
├──                  # Use the default camera by opencv
├── data
│   ├── data_dlib                         # Dlib's model
│   │   ├── dlib_face_recognition_resnet_model_v1.dat
│   │   └── shape_predictor_68_face_landmarks.dat
│   ├── data_faces_from_camera            # Face images captured from camera (will generate after step 1)
│   │   ├── person_1
│   │   │   ├── img_face_1.jpg
│   │   │   └── img_face_2.jpg
│   │   └── person_2
│   │       └── img_face_1.jpg
│   │       └── img_face_2.jpg
│   └── features_all.csv                  # CSV to save all the features of known faces (will generate after step 2)
├── README.rst
└── requirements.txt                      # Some python packages needed

用到的 Dlib 相关模型函数:

. Dlib 正向人脸检测器 (based on HOG), output:

.. code-block:: python

  detector = dlib.get_frontal_face_detector()
  faces = detector(img_gray, 0)

. Dlib 人脸 landmark 特征点检测器, output: ,

will use shapepredictor68facelandmarks.dat

.. code-block:: python

  # This is trained on the ibug 300-W dataset (
  # Also note that this model file is designed for use with dlib's HOG face detector.
  # That is, it expects the bounding boxes from the face detector to be aligned a certain way, the way dlib's HOG face detector does it.
  # It won't work as well when used with a face detector that produces differently aligned boxes,
  # such as the CNN based mmod_human_face_detector.dat face detector.

predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_68_face_landmarks.dat") shape = predictor(img_rd, faces[i])

. Dlib 特征描述子 Face recognition model, the object maps human faces into 128D vectors

.. code-block:: python

  face_rec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")

Python 源码介绍如下:


进行 Face register / 人脸信息采集录入

  • 请注意存储人脸图片时,矩形框不要超出摄像头范围,要不然无法保存到本地;
  • 超出会有 "out of range" 的提醒;



  • 会生成一个存储所有特征人脸数据的 "features_all.csv";
  • size: n*128 , n means n people you registered and 128 means 128D features of the face


这一步将调用摄像头进行实时人脸识别; / This part will implement real-time face recognition;

  • Compare the faces captured from camera with the faces you have registered which are saved in "features_all.csv"

  • 将捕获到的人脸数据和之前存的人脸数据进行对比计算欧式距离, 由此判断是否是同一个人;

. facerecofromcameraotsingleperson/

区别于 (对每一帧都进行检测+识别),只会对初始帧做检测+识别,对后续帧做检测+质心跟踪;

. (optional)

调用摄像头进行实时特征描述子计算; / Real-time face descriptor computation;



. 如果希望详细了解 dlib 的用法,请参考 Dlib 官方 Python api 的网站 / You can refer to this link for more information of how to use dlib:

. Windows下建议不要把代码放到
, 可能会出现权限读取问题 / In windows, we will not recommend that running this repo in dir

. 代码最好不要有中文路径 / No chinese characters in your code directory

. 人脸录入的时候先建文件夹再保存图片, 先
/ Press

. 关于人脸识别卡顿 FPS 低问题, 原因是特征描述子提取很费时间, 光跑 中 facerecomodel.computeface_descriptor 在 CPU: i7-8700K 得到的最终 FPS: 5~6 (检测在 0.03s, 特征描述子提取在 0.158s, 和已知人脸进行遍历对比在 0.003s 左右), 所以主要提取特征时候耗资源, 可以用 OT 去做追踪,而不是对每一帧都做检测+识别

可以访问我的博客获取本项目的更详细介绍,如有问题可以邮件联系我 / For more details, please refer to my blog (in chinese) or mail to me :

  • Blog:

  • 关于 OT 部分的更新在 Blog:

  • Mail: [email protected] ( Dlib 相关 repo 问题请联系 @foxmail 而不是 @intel )

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