/Dlib_face_recognition_from_camera

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

Primary LanguagePythonMIT LicenseMIT

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

    introduction/face_register.png

    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;

    introduction/face_register_warning.png

  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:

    introduction/face_reco_single.png

    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:

    introduction/face_reco_single_ot.png

    当多张人脸 / When multi-faces:

    introduction/face_reco_multi.png

    利用 OT 来实现 / When multi-faces with OT:

    introduction/face_reco_multi_ot.png

    定制显示名字, 可以写中文 / Customize names:

    introduction/face_reco_with_name.png

** 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 (no OT, 每一帧都进行检测+识别) / The design of this repo:

introduction/overview.png

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:

introduction/overview_with_ot.png

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

  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

  6. 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

    python3 face_reco_from_camera_ot_single_person.py
python3 face_reco_from_camera_ot_multi_people.py

About Source Code

Repo 的 tree / 树状图:

.
├── get_faces_from_camera.py                        # Step 1. Face register
├── features_extraction_to_csv.py                   # Step 2. Feature extraction
├── face_reco_from_camera.py                        # Step 3. Face recognizer
├── face_reco_from_camera_ot_single_person.py       # Step 3. Face recognizer with OT for single person
├── face_reco_from_camera_ot_multi_people.py        # Step 3. Face recognizer with OT for multi people
├── 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:

  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 landmark 特征点检测器, 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. 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:

  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;

  4. face_reco_from_camera_ot_single_person/multi_people.py:

    区别于 face_reco_from_camera.py (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;

  5. (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

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. 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 face_descriptor_from_camera.py 中 face_reco_model.compute_face_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 :

Thanks for your support.