Explainable Artificial Intelligence: How Face Masks are Detected via Deep Neural Networks


Authors : Ahmet Haydar Ornek; Mustafa Celik; Murat Ceylan

Volume/Issue : Volume 6 - 2021, Issue 9 - September

Google Scholar : http://bitly.ws/gu88

Scribd : https://bit.ly/2ZD1bys

The image classification has become a wellknown process with the development of deep neural networks. Although classification studies above 90% accuracy are realized, their explainable side is still an open area which means the classification process are not known by researchers. In this study, we show what a deep neural network model learns from face images to classify them into with mask and without mask classes using last convolutional layers of the model. As a deep neural network model ResNet-18 was selected and the model was trained with 18600 balanced face images belonging two classes and tested with 4540 face images different from training images. The model's test results are obtained as 95.16% sensitivity, 96.69% specificity, 96.58% accuracy. With the created activation maps it is clearly seen that the model learns face structure for images without mask and mask structure for images with mask.

Keywords : Classification; Covid-19; Explainable Artificial Intelligence; Transfer Learning

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