Face recognition from camera with Dlib
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Introduction
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Detect and recognize single/multi-faces from camera;
调用摄像头进行人脸识别, 支持多张人脸同时识别;
#. Tkinter 人脸录入界面, 支持录入时设置姓名 / Face register GUI with Tkinter, support setting name when registering
.. image:: introduction/face_register_tkinter_GUI.png
:align: center
#. 简单的 OpenCV 摄像头人脸录入界面 / Simple face register GUI with OpenCV
.. 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/face_register_warning.png
:align: center
#. 提取特征建立人脸数据库 / Generate face database from images captured
#. 利用摄像头进行人脸识别 / Face recognizer
face_reco_from_camera.py, 对于每一帧都做检测识别 / Do detection and recognition for every frame:
.. image:: introduction/face_reco.png
:align: center
face_reco_from_camera_single_face.py, 对于人脸<=1, 只有新人脸出现才进行再识别来提高 FPS / Do re-reco only for new single face:
.. image:: introduction/face_reco_single.png
:align: center
face_reco_from_camera_ot.py, 利用 OT 来实现再识别提高 FPS / Use OT to instead of re-reco for every frame to improve FPS:
.. image:: introduction/face_reco_ot.png
:align: center
定制显示名字, 可以写中文 / Show chinese name:
.. image:: introduction/face_reco_chinese_name.png
:align: center
** 关于精度 / About accuracy:
* When using a distance threshold of ``0.6``, the dlib model obtains an accuracy of ``99.38%`` 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.
Overview
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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/overview_with_ot.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;
Steps
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#. 安装依赖库 / Install some python packages needed
.. code-block:: bash
pip install -r requirements.txt
#. 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 https://github.com/coneypo/Dlib_face_recognition_from_camera
#. 进行人脸信息采集录入, Tkinter GUI / Register faces with Tkinter GUI
.. code-block:: bash
python3 get_faces_from_camera_tkinter.py
#. 进行人脸信息采集录入, OpenCV GUI / Register faces with OpenCV GUI
.. code-block:: bash
python3 get_face_from_camera.py
#. 提取所有录入人脸数据存入 "features_all.csv" / Features extraction and save into "features_all.csv"
.. code-block:: bash
python3 features_extraction_to_csv.py
#. 调用摄像头进行实时人脸识别 / Real-time face recognition
.. code-block:: bash
python3 face_reco_from_camera.py
#. 对于人脸数<=1, 调用摄像头进行实时人脸识别 / Real-time face recognition (FPS improved)
.. code-block:: bash
python3 face_reco_from_camera_single_face.py
#. 利用 OT 算法, 调用摄像头进行实时人脸识别 / Real-time face recognition with OT (FPS improved)
.. code-block:: bash
python3 face_reco_from_camera_ot.py
About Source Code
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Repo 的 tree / 树状图:
::
.
├── get_faces_from_camera.py # Step 1. Face register GUI with OpenCV
├── get_faces_from_camera_tkinter.py # Step 1. Face register GUI with Tkinter
├── features_extraction_to_csv.py # Step 2. Feature extraction
├── face_reco_from_camera.py # Step 3. Face recognizer
├── face_reco_from_camera_single_face.py # Step 3. Face recognizer for single person
├── face_reco_from_camera_ot.py # Step 3. Face recognizer with OT
├── face_descriptor_from_camera.py # Face descriptor computation
├── how_to_use_camera.py # 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: <class 'dlib.dlib.rectangles'>
.. 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: <class 'dlib.dlib.full_object_detection'>,
will use shape_predictor_68_face_landmarks.dat
.. code-block:: python
# This is trained on the ibug 300-W dataset (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/)
# 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:
#. get_face_from_camera.py:
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;
#. features_extraction_to_csv.py:
从上一步存下来的图像文件中, 提取人脸数据存入CSV;
* 会生成一个存储所有特征人脸数据的 "features_all.csv"；
* size: n*129 , n means nx faces you registered and 129 means face name + 128D features of this face
#. face_reco_from_camera.py:
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;
#. face_reco_from_camera_single_face.py:
针对于人脸数 <=1 的场景, 区别于 face_reco_from_camera.py (对每一帧都进行检测+识别), 只有人脸出现的时候进行识别;
#. face_reco_from_camera_ot.py:
只会对初始帧做检测+识别, 对后续帧做检测+质心跟踪;
#. (optional) face_descriptor_from_camera.py
调用摄像头进行实时特征描述子计算; / Real-time face descriptor computation;
More
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Tips:
#. 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: http://dlib.net/python/index.html
#. Modify log level to ``logging.basicConfig(level=logging.DEBUG)`` to print info for every frame if needed (Default is ``logging.INFO``)
#. 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 ``N`` 再 ``S`` / Press ``N`` before ``S``
#. 关于 `face_reco_from_camera.py` 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 face_descriptor_from_camera.py 中
face_reco_model.compute_face_descriptor 在我的机器上得到的平均 FPS 在 5 左右 (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 (使用`face_reco_from_camera_ot.py`), 而不是对每一帧都做检测+识别, 识别的性能从 20 FPS -> 200 FPS
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: https://www.cnblogs.com/AdaminXie/p/9010298.html
* 关于 OT 部分的更新在 Blog: https://www.cnblogs.com/AdaminXie/p/13566269.html
* Mail: coneypo@foxmail.com ( Dlib 相关 repo 问题请联系 @foxmail 而不是 @intel )
* Feel free to creatE issue or PR for this repo :)
Thanks for your support.