• faceRecogDaemon
  • description
    • Problem
    • Possible solution
    • Features
      • user side
      • technical side
  • installation
    • download & start
    • configuration
  • API
    • face recognition
      • /api/check
      • /api/check?stream=1
    • training
      • /api/training?getdata=true
      • /api/training?retrain=true
      • /api/training?rename=true&image=mmmmm.jpg&name=Joe
      • /api/get_train_image?person=name&filename=name01.png
      • /api/get_train_image?person=name&number=0
    • recent recognitions
      • /api/get_recent_image
      • /api/get_recent_image?json
      • /api/get_recent_image?json&limit=30
      • /api/get_recent_image?number=0
      • /api/get_recent_image?filename=name
      • /api/get_recent_image?number=0&original=true
    • images
      • /api/get_image?file=name
  • Links

Just started as a little application for easy managing face recognition outputs.

You have a video/ image stream of a camera or something else. And you want to analyse the images for known faces. And get needed infos for further triggering for e.g. a smart home system to open a door or similar things.

face recognition daamon ;-)

• bind with image streams or NEW with rtsp stream, see config
• overview of recent historic recognized faces
  • change seen faces to other persons for next training
  • change seen faces to new persons for next training (coming soon)
• manage training data
  • remove images from person folder
  • move images to other persons
• manage settings (editing - coming soon)
• show api information

screenshots of client:

• for the recognition itself the library of https://github.com/ageitgey/face_recognition is used
• api for all stuff provided
• client in hmtl/ javascript full responsive and prepared for web app
• 3-tier architecture - file-db, backend/ middleware, client ( not fully at this time ;-) )
  • "file-db" - primitve folder driven training data for images + archive for recognized faces
  • backend/ middleware - serves the functions over an api
  • client - html/ javascript web client prepared for a web app

The application is written in Python. So you need an already installed environment as described here: https://www.python.org/about/gettingstarted/ - needed version >= 3.6

If you running an ubuntu maybe you need for compiling dlib (only one time)

    sudo apt install python3.7-dev

Donwload 'face recognition daemon' application the last release at https://github.com/ohAnd/faceRecogDaemon/releases

Update pip to the latest

    python -m pip3 install --upgrade pip

during the installation dlib will be installed (need's some time for compiling ;-) )

intall needed modules

    pip3 install -r requirements.txt

for quick start:

• create a path for known faces outside the app directory e.g. /path/to/knownFaces and put the path to config see below
  • inside this path create a dirextory for each person you want to recognize
  • in this subfolders you have to put some images for training
  • images must have only one face showing, otherwise it it will be ignored by training

      Structure:
            </path/to/knownFaces>/
            |-- <person1>/
            |   |-- <somename1>.jpeg
            |   |-- <somename2>.png
            |   |-- ...
            |-- <person2>/
            |   |-- <somename1>.jpeg
            |   |-- <somename2>.jpeg
            |-- ...
    

• create a archive path outside the app directory e.g. /path/to/imageArchive and put the path to config see below

start the server (service installation coming soon)

    bash://<paht/to/your/download>> ./faecRecogDaemon.py

create a config file in application root directory

    touch config.ini

with following content

    # config file for face recog daemon
    [webserver]
    Port: 5001

    [imageSource]
    urlToImageSrc = <your_path_to_image_src> # e.g. http://<urlTocam>:port/image.cgi
    // urlToImageSrc = <your_path_to_image_src> # e.g. rtsp://user:pass@<ip-address>:<port>/media.amp

    [faceRecognition]
    knnDistance = 0.50

    [directories]
    pathToKnownFaces = <your_path>
    pathToImageArchive = <your_path>

trigger new face recognization with a fresh image from source
return: JSON

{
  "checkDetails": {
    "NumberOfFaces": 0, 
    "imageToCheck": "http://<source image>", 
    "linkToArchivedImage": ""
  }, 
  "checkResults": {
    "linkToResultImage": "http://<host of face recog daemon>/api/get_image?file=/noFaces/<archived image name>.jpg", 
    "personsFound": {}
  }, 
  "status": "no faces found"
}

get online face recognization, return the result image directly
return: image

get all training src data as summary in JSON

{
  "persons": {
    "<nameOfPerson1>": {
      "count": 26, 
      "imagelinks": [
        "<baseImage>.png", 
        ...
        "<baseImage_n_>.jpg", 
        "<baseImage_m_>.jpg"
      ]
    }, 
    "<nameOfPerson2>": {
      "count": 10, 
      "imagelinks": [
        "<baseImage>.png", 
        ...
        "<baseImage_n_>.jpg", 
        "<baseImage_m_>.jpg"
      ]
    }
  }
}

retrigger the training of given images

move image from history to training data with given person name

get trained image by person and filename

get trained image by number

get most recent image

get an overview of recent images in JSON
(default limited to 10, if less images available it is shortend)

get an overview of recent images in JSON with given limit of resopnse
e.g. 30 entries (hard limited to 1000)

get most recent image - higher number means older image

get most recent image with new face recognition fpr filename

get most recent image without image manipulation (timestamp face frame/ naming)

get image file with name

source for used libs:

• https://github.com/ageitgey/face_recognition
• https://github.com/ageitgey/face_recognition/blob/master/examples/face_recognition_svm.py
• https://face-recognition.readthedocs.io/en/latest/face_recognition.html

used images:

• https://www.freepik.com/premium-vector/biometric-person-identification-facial-recognition-concept-futuristic-low-polygonal-human-face_4847694.htm
• ...