/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

调用摄像头进行人脸识别, 支持多张人脸同时识别 / Detect and recognize single or multi faces from camera;

  1. Tkinter 人脸录入界面, 支持录入时设置 (中文) 姓名 / Face register GUI with Tkinter, support setting (chinese) name when registering

    introduction/face_register_tkinter_GUI.png

  2. 简单的 OpenCV 摄像头人脸录入界面 / Simple face register GUI with OpenCV, tkinter not needed and cannot set name

    introduction/face_register.png

    离摄像头过近, 人脸超出摄像头范围时, 会有 "OUT OF RANGE" 提醒 / Too close to the camera, or face ROI out of camera area, will have "OUT OF RANGE" warning;

    introduction/face_register_warning.png

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

  4. 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:

    introduction/face_reco.png

    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:

    introduction/face_reco_single.png

    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:

    introduction/face_reco_ot.png

    定制显示名字, 可以写中文 / Show chinese name:

    introduction/face_reco_chinese_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) / Design of this repo, do detection and recognization for every frame:

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) / OT used:

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 / Use OT can save the time for face descriptor computation to improve FPS;

Steps

  1. 下载源码 / Git clone source code

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

  2. 安装依赖库 / Install some python packages needed

    pip install -r requirements.txt

  3. 进行人脸信息采集录入, Tkinter GUI / Register faces with Tkinter GUI

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

python3 get_faces_from_camera_tkinter.py

  4. 进行人脸信息采集录入, OpenCV GUI / Register faces with OpenCV GUI, same with above step

    python3 get_face_from_camera.py

  5. 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

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

    python3 face_reco_from_camera.py

  7. 对于人脸数<=1, 调用摄像头进行实时人脸识别 / Real-time face recognition (Better FPS compared with face_reco_from_camera.py)

    python3 face_reco_from_camera_single_face.py

  8. 利用 OT 算法, 调用摄像头进行实时人脸识别 / Real-time face recognition with OT (Better FPS)

    python3 face_reco_from_camera_ot.py

About Source Code

代码结构 / Code structure:

.
├── 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 related functions used in this repo:

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

    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'> / 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])

  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 / Source code:

  1. get_face_from_camera.py:

    人脸信息采集录入 / Face register with OpenCV GUI

    • 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. get_faces_from_camera_tkinter.py:

    进行人脸信息采集录入 Tkinter GUI / Face register with Tkinter GUI

  3. 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 / Extract features 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

  4. 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;

    • 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;

  5. 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;

  6. 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;

  7. (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

  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. Modify log level to logging.basicConfig(level=logging.DEBUG) to print info for every frame if needed (Default is logging.INFO)
  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 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 visit my blog (in chinese) or send mail to coneypo@foxmail.com:

Thanks for your support.