Face recognition from camera with Dlib

Added Introduction

利用 Python 开发，借助 Dlib 库捕获摄像头中的人脸，提取人脸特征，通过计算特征值之间的欧氏距离，来和预存的人脸特征进行对比，判断是否匹配，达到人脸识别的目的；

完成的功能
- 新的人脸数据集和识别模型
- 做到视频识别，实时显示人脸信息
- 可摄像头添加人脸，且可改名
- 可通过文件夹方式批量添加人脸
- GUI化，基本无卡顿

Former 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;

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;
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:

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

安装依赖库 / Install some python packages if needed

pip3 install opencv-python
pip3 install scikit-image
pip3 install dlib
pip install wxPython-4.0.0a2.dev3038+953a2e5-cp27-cp27mu-linux_x86_64.whl

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
```
进行人脸信息采集录入 / Register faces
```
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"
```
python3 features_extraction_to_csv.py
```
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:

.
├── main_interface.py               # Step0. Start First
├── 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
│   ├── main_figure_interface.png
│   ├── main_interface.png
│   └── overview.png
├── README.md
└── 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'>
```
detector = dlib.get_frontal_face_detector()
faces = detector(img_gray, 0)
```

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])

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: