Python实现多人脸识别系统-蒲公英云

资源下载地址：https://download.csdn.net/download/sheziqiong/88284343 资源下载地址：https://download.csdn.net/download/sheziqiong/88284343 Face recognition from camera with Dlib

Introduction

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 (中文) 姓名

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简单的 OpenCV 摄像头人脸录入界面

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离摄像头过近, 人脸超出摄像头范围时, 会有 “OUT OF RANGE” 提醒

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提取特征建立人脸数据库
利用摄像头进行人脸识别

face_reco_from_camera.py, 对于每一帧都做检测识别

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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

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face_reco_from_camera_ot.py, 利用 OT 来实现再识别提高 FPS

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定制显示名字, 可以写中文

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关于精度 :

当使用 0.6 的距离阈值时，dlib 模型在标准 LFW 人脸识别基准上获得 99.38% 的精度。

关于算法

基于 Residual Neural Network / 残差网络的 CNN 模型;
This model is a ResNet network with 29 conv layers.

本质上是 ResNet-34 网络的一个版本，来自 He，Zhang，Ren 和 Sun 的论文 Deep Residual Learning for Image Recognition，去掉了几层，每层过滤器的数量减少了一半。

概述

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)

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

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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

/ Git clone source code

```{.bash}
git clone https://git.writebug.com/goodwill/DlibFaceRecognition.git
1. 安装依赖库
  
```{.bash}
pip install -r requirements.txt

进行人脸信息采集录入,

# Install Tkinter
sudo apt-get install python3-tk python3-pil python3-pil.imagetk
python3 get_faces_from_camera_tkinter.py

进行人脸信息采集录入, OpenCV GUI
```
python3 get_face_from_camera.py
```
提取所有录入人脸数据存入 features_all.csv
```
python3 features_extraction_to_csv.py
```
调用摄像头进行实时人脸识别
```
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
```
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
```
python3 face_reco_from_camera_ot.py
```

源代码相关

代码结构 :

.
├── 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
<class 'dlib.dlib.rectangles'> / Dlib frontal face detector
```
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'> / Dlib face landmark
predictor, 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])

Dlib 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

get_face_from_camera.py:

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;
get_faces_from_camera_tkinter.py:

进行人脸信息采集录入 Tkinter GUI
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
from face images saved in step 1;
- 会生成一个存储所有特征人脸数据的 features_all.csv
- Size: n*129 , n means n 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;
real-time face recognition;
- 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;
- Compare the faces captured from camera with the faces you have
  registered which are saved in features_all.csv;
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;
computation;