The Efficient Implementation of Face Mask Detection Using MobileNet

Abstract

Under the condition of the global pandemic, Wearing the mask in the public area is the direct and ordered action to prevent the spreading of the virus. Therefore, developing the machine learning model to detect whether the mask is appropriately wearing is practical and meaningful. This paper unfolded the mask recognition based on the MobileNet model, which belongs to one of the branches of Convolutional Neural Network, CNN for short, in the deep learning field. With the background and tremendous needs of face mask detection, in the beginning, we go through the review of the MobileNet's history and the practicability of it on demanding functions. After that, we explained in a more detailed way to help gain a better understanding of MobileNet's structure and how those differences make it outstanding. During the experiment, approximately 9000 images were used as inputs by the model for training and optimizing. The result turns to the less weighted model presenting an acceptable accuracy of 87.96% and 93.5% for testing whether a person wears a mask and whether the mask is covered correctly correspondingly. We also discovered several shortcomings and limitations during the testing process. Those problems and some of the possible further directions of MobileNet will be discussed. As a result, the MobileNet shows the huge probability for further improvement to be equipped into the small-scale but sophisticated utensil.