Three Classification Models for Masked Faces
Keywords:
Convolutional Neural Networks (CNNs), Classification Models, Masked/Unmasked Classification, IdentificationAbstract
In recent years, deep learning technology has grown quickly and made great progress in the field of computer vision. Convolutional neural networks (CNNs), Residual networks (ResNets), Inception networks, and MobileNet are among the deep learning classification models that have gained widespread use for picture classification applications. The COVID-19 pandemic in early 2020 led to the widespread use of face masks as a vital containment strategy. The use of masks has given rise to two problems. The first is whether or not the individual is donning a mask. The second problem is that these masks cover almost half of the human face, which obviously modifies facial appearance. In this paper, we propose three proposed classification models (Tree Models). The proposed models are tested in three distinct cases. Case 1 (2 Outputs), the models are evaluated for Masked/Unmasked classification. Case 2 (50 Outputs) is aiming for recognition of 50 different persons. The models are tested on an expanded set of subjects, from 50 to 85, in Case 3 (85 Outputs).
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