DO Not Touch your face (DONT)

• Download link (executable file) : https://www.dropbox.com/s/2rfqog5kxuq4hmw/dont-release.ver.0.3.zip?dl=0
• DONT(R) was developed to overcome 2019 pandemic of SARS-CoV-2 in the world by MI2RL@Asan Medical Center, South Korea
• Pre-trained network is available (weights folder)
• Demo video : https://youtu.be/Yn7jqsNAmNk
• Rationale
  • A Study Quantifying the Hand-to-Face Contact Rate and Its Potential Application to Predicting Respiratory Tract Infection (https://www.tandfonline.com/doi/full/10.1080/15459620802003896)
  • Controlling the novel A (H1N1) influenza virus: don't touch your face! (https://www.journalofhospitalinfection.com/article/S0195-6701(09)00255-2/abstract)
  • Hand Hygiene Practices in a Neonatal Intensive Care Unit: A Multimodal Intervention and Impact on Nosocomial Infection (https://pediatrics.aappublications.org/content/114/5/e565.short)

NEWS (20.03.30) : DONT ver.0.4

• Add MobileNet version.

  • Can recognize face-touching actions in 0.07 sec with Intel(R) Core i7-6700 CPU 3.40GHz or higher (92% accuracy)

• TO DO:

  • Upload a paper on Arxiv about this project

  • Add sound effects for alarm

  • Add a "report" function based on 24-hour monitoring mode

  • Increase the supported languages (Eng, Jap) for GUI

  • Try video clips from ceiling-mounted security cameras

  • Develop a lightweight model for mobile phones, CPU-only machine

Installation

# Clone this repository and enter it:
$ git clone https://github.com/mi2rl/DONT.git
# Set up the environment
$ conda create -n [your virtual environment name] python3
# Activate the environment
$ conda activate fta_gpu
# Install all the dependencies
$ pip install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
$ pip install -r requirements.txt

Quick Guide

• GUI program can be run using

  $ python main.py

• GUI

  

  
  * Run/pause the classifier by 'Start'/'Pause' button

  • The result from action classifier will be shown

  • Turn on Webcam display in live using 'Camera' button

  
  

• Run Windows (EXE) App

  • Unzip a downloaded .zip file, then run "DONT.exe"

    

    
    

• When Webcam display window is activated, other buttons are deactivated (Terminate Webcam display mode and press the buttons)

• If the webcam is not ready, the software does NOT work properly.

Further details

• Datasets

  • In order to make the training dataset, MI2RL members and many collaborators contributed. As a result, we gathered a total of 190,000 images
  • Video clips were recorded at more than 10 different locations
  • Action classes : 11 classes
    • Overall classes : drinking, picking up phone, removing mask, resting chin on hand, rubbing eyes, touching glasses, touching hairs, touching keyboard, touching nose, touching phone, wearing mask
    • Touching actions : picking up phone, resting chin on hand, rubbing eyes, touching hairs, touching nose

• Action Classification Network (I3D / MobileNet3)

  • I3D Network (https://github.com/deepmind/kinetics-i3d)

    • Training phase

      • The number of frames in each stack for 3D CNN : 16
      • Data augmentation
        • Step in frames between each clip : 4
        • Color distortion
        • Rotation

    • Inference Phase

      • The number of frames in each stack for 3D CNN : 24

  • MobileNet3 (https://github.com/d-li14/mobilenetv3.pytorch)

    • Training phase

      • The number of frames in each stack for CNN : 3

        • Make 3 channel image as following time interval (3, 5, 7, 9, 11, 13, 15)

        • Each time interval, we make 20,000 images. So, the number of training images are 280,000.

          
          

      • Datset configuration

        
        

        
        

      • Inference Phase

        • Time interval (stride): 3 frame

• H/W specification

  • Test specification.

    • GPU : Geforce GTX 960 4GB
      • CPU : Intel(R) Core i7-6700 CPU 3.40GHz
      • OS : Linux Ubuntu 18.04
      • Inference
        • I3D Network
          • 0.07~0.085 sec on GPU
            • 1.4~1.5 sec on CPU
            • CPU usage ≈ 35%
            • GPU memory usage ≈ 1.1GB
        • MobileNet v3
          • 0.03~0.04 sec on GPU
          • 0.07~0.09 sec on CPU
          • CPU usage ≈ 4%
          • GPU memory usage ≈ 520MB

  • Minimum specification

    • Geforce GTX 960 4GB
    • Intel(R) Core i7-6700 CPU 3.40GHz
    • OS : Linux / Windows
  
  

Experimental Results

Confusion matrix : binary-class

Limitations

• DONT began at 2020.03.05. We decided that it would be more desirable to call for joint efforts through faster release than creating high-performance programs, so we decided to proceed with the disclosure despite the lack of progress.

How to donate your data

• For more robust model for DONT, we need more data from different environments and people. If you want to donate your data, please send it to namkugkim@gmail.com. Your privacy will be strictly protected, as strong as possible.
  

Guideline for data donation

• Please take a video and send it to the e-mail address above.
• Recording process is as follows.
  • Wearing mask -> (With a mask) -> Touching nose -> Resting chin on hand -> Rubbing eyes -> Touching hairs -> Drinking water-> Touching phone -> Picking up phone -> Touching keyboard -> (Without a mask) -> Touching nose -> Resting chin on hand -> Rubbing eyes -> Touching hairs -> Drinking water-> Touching phone -> Picking up phone -> Touching keyboard
  • Moderate video recording time is about 90 seconds.
  • Example : Gudieline for video recording

Project Contributors

• Artificial Neural Network and GUI development
  • Sungman Cho(dev.sungman@gmail.com), Minjee Kim(minjeekim00@gmail.com)
• Data acquisition and annotation
  • Dr. Joonmyeong Choi(jm5901@gmail.com), Taehyung Kim(kimtaehyeong62@gmail.com), Juyoung Park(godoctorsam@gmail.com)
• General Management: Prof. Namkug Kim(namkugkim@gmail.com)
• Data provider : 37 MI2RL researchers, Prof. Dong-woo Seo(Dept. of Emergency Medicine at AMC), Prof. Namkug Kim(Dept. of Radiology at AMC), Prof. Sunjin Lim(SNU Dental Hospital)