A transfer learning approach with convolutional neural network for Face
  Mask Detection
Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around
the world, the world has faced an enormous crisis. To prevent the spread of the
coronavirus, the World Health Organization (WHO) has introduced the use of
masks and keeping social distance as the best preventive method. So, developing
an automatic monitoring system for detecting facemasks in some crowded places
is essential. To do this, we propose a mask recognition system based on
transfer learning and Inception v3 architecture. In the proposed method, two
datasets are used simultaneously for training including the Simulated Mask Face
Dataset (SMFD) and MaskedFace-Net (MFN) This paper tries to increase the
accuracy of the proposed system by optimally setting hyper-parameters and
accurately designing the fully connected layers. The main advantage of the
proposed method is that in addition to masked and unmasked faces, it can also
detect cases of incorrect use of mask. Therefore, the proposed method
classifies the input face images into three categories. Experimental results
show the high accuracy and efficiency of the proposed method; so, this method
has achieved an accuracy of 99.47% and 99.33% in training and test data
respectively
Abolfazl Younesi, Reza Afrouzian, Yousef Seyfari
# A transfer learning approach with convolutional neural network for Face Mask Detection

###### Abstract

Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced a huge crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detection of facemask in some crowded places is essential. To do this, we propose a mask recognition system based on transfer learning and Inception v3 architecture. In the proposed method, two datasets are used simultaneously for training including: Simulated Mask Face Dataset (SMFD) and MaskedFace-Net (MFN),this paper tries to increase the accuracy of the proposed system by optimally setting hyper-parameters and accurately designing the fully connected layers. The main advantage of the proposed method is that in addition to masked and unmasked face, it can also detect cases of incorrect use of mask. Therefore, the proposed method classifies the input face images into three categories. Experimental results show the high accuracy and efficiency of the proposed method; so that, this method has achieved to accuracy of 99.47% and 99.33% in training and test data respectively.

M 2021 177