Detection-of-Mask-Wearing-using-CNN

A Convolution Neural Network (CNN) is applied to detect whether the masks are correctly worn.

Dataset

Over 3000 mask-wearing faces from 682 images were included in this project.
The images all comes from Medical Masks Dataset, which were originally collected by Cheng Hsun Teng from Eden Social Welfare Foundation at -> https://public.roboflow.com/object-detection/mask-wearing

Our purpose is to classify 3 types of faces, including correct mask-wearing, wrong mask-wearing & no mask-wearing

Type (notation)	Num for Train	Num for Test
Correct mask-wearingl ("Good")	2864	283
Wrong mask-wearing ("None")	104	22
No mask-wearing ("Bad")	578	89

Execution & Overall Structure of system

1. Image Preprocessing : Resize Images to [64,64]

   python3 Image_Preprocess.py
   

2. CNN for Classification : training the model with Torch package

   python3 CNN.py
   

Image Preprocessing

1. Extract faces (sub-image) from picture according to the bounding box provided by Medical Masks Dataset with openCV
2. Apply Cubic Spline Interpolation to resize sub-images to 64 x 64 pixel.
3. Shift the mean of pixels from all sub-images to zero.
   

CNN for Classification

• Concept of Convolution Neural Network
  

• Structure used in this project
  

• Imbalanced Dataset Problem

  From the Table given above, it was found that the data amount of "No mask-wearing" is least, which resulted in the poor sensitivity of Test data because the model would focus on the majority class and ignore the minority class.

  Type (notation)	Train Sensitivity	Test Sensitivity
  Correct mask-wearingl ("Good")	99.2%	96.5%
  Wrong mask-wearing ("None")	97.4%	95.5%
  No mask-wearing ("Bad")	80.8%	50%

  To tackle this problem, the heavier penalty is imposed on the minority class to emphasize the learning of the minority class via re-weight scheme.
  The implmentation is shown as:

  

• Resluts after the introduction of re-weight scheme

  Type (notation)	Train Sensitivity	Test Sensitivity
  Correct mask-wearingl ("Good")	94.3%	90.1%
  Wrong mask-wearing ("None")	98.4%	98.9%
  No mask-wearing ("Bad")	96.2%	68.2%