Skip to content

create an occluded face images to simulate covid-19 face wear and program a face recognition system that utilizes the data.

Notifications You must be signed in to change notification settings

ijhrecto/Occluded-Face-Recog-with-Image-Data-Simulation

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

21 Commits
 
 
 
 
 
 

Repository files navigation

Occluded-Face-Recog-with-Image-Data-Simulation

Aim of Study

The aim of this study is to investigate current problems and study the accuracy of facial recognition on occluded facial image datasets. The scope of this study is the usage of artificial neural networks, and occlusions on the lower face area manifested by the usage of face masks for COVID-19. The researchers’ goal is to contribute by mitigating problems, such as usage of masks, for technologies using facial recognition on controlled environments like biometrics and authentication systems for workplaces and schools.

Process

Dataset

Facial Images - Georgia Tech Face

Mask images

  • included in the downloads
  • functional design : N95 mask and medical masks
  • abstract design : random shapes and patterns
  • mouth design : mouths images or arts printed as a design

Data Preprocessing

  • The raw image data contains a person with a background, so we cropped and aligned the face.
  • PIL and Dlib was used to process the raw images
  • We created our own simulated occluded face images (occluded facial areas with black) for one our training experiments.
  • We also simulated and superimpose a face mask on the raw facial images to create our own masked occluded facial image dataset

Experiments

  • We used a 22 layer neural network architecture to train our model.
  • We then classified the face using Support Vector Machine.
  • There are 6 scenarios done to train and test different facial occlusion and facial mask.

About

create an occluded face images to simulate covid-19 face wear and program a face recognition system that utilizes the data.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages