Face recognition from camera with Dlib

Introduction

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;

1. 摄像头人脸录入 / Face register

   

   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;

   

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

3. 利用摄像头进行人脸识别 / Face recognizer

   当单张人脸 / When single-face:

   

   当多张人脸 / When multi-faces:

   一张已录入人脸 + 未录入 unknown 人脸 / 1x known face + 2x unknown face:

   

   同时识别多张已录入人脸 / multi-faces recognition at the same time:

   

** 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

此项目中人脸识别的实现流程 / The design of this repo:

Steps

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

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

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

   git clone https://github.com/coneypo/Dlib_face_recognition_from_camera
   

3. 进行人脸信息采集录入 / Register faces

   python3 get_face_from_camera.py
   

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

   python3 features_extraction_to_csv.py
   

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

   python3 face_reco_from_camera.py
   

About Source Code

Repo 的 tree / 树状图:

.
├── get_faces_from_camera.py        # Step1. Faces register
├── features_extraction_to_csv.py   # Step2. Features extraction
├── face_reco_from_camera.py        # Step3. Faces recognition
├── 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_5_face_landmarks.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)
├── introduction                    # Some files for readme.rst
│   ├── Dlib_Face_recognition_by_coneypo.pptx
│   ├── face_reco_single_person_customize_name.png
│   ├── face_reco_single_person.png
│   ├── face_reco_two_people_in_database.png
│   ├── face_reco_two_people.png
│   ├── get_face_from_camera_out_of_range.png
│   ├── get_face_from_camera.png
│   └── overview.png
├── README.rst
└── requirements.txt                # Some python packages needed

用到的 Dlib 相关模型函数:

1. Dlib 正向人脸检测器 (based on HOG), output: <class 'dlib.dlib.rectangles'>

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

2. Dlib 人脸预测器, output: <class 'dlib.dlib.full_object_detection'>, will use shape_predictor_68_face_landmarks.dat

   # 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])
   

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

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

Python 源码介绍如下:

1. 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;

2. 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*128 , n means n people you registered and 128 means 128D features of the face

3. 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;

More

Tips:

1. 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
2. Windows下建议不要把代码放到 C:\, 可能会出现权限读取问题 / In windows, we will not recommend that running this repo in dir C:\
3. 代码最好不要有中文路径 / No chinese characters in your code directory
4. 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
5. 修改显示人脸姓名, 参考这个 patch 修改代码 / If you want to customize the names shown instead of "Person 1", "Person 2"..., please apply this patch: https://github.com/coneypo/Dlib_face_recognition_from_camera/commit/0351cc0f1a1c3a106102c0671dd19edd5866fa93

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
• Mail: coneypo@foxmail.com ( Dlib 相关 repo 问题请联系 @foxmail 而不是 @intel )

仅限于交流学习, 商业合作勿扰;

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