COVID-19 Face Mask Restoration Using Pix2pix model
2nd International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)
; : 157-161, 2021.
Article
in English
| English Web of Science | ID: covidwho-1883118
ABSTRACT
Accurate facial recognition can effectively help the population combat the disease by offering risk-free phone usage, access controls, etc. In the era of COVID-19, a mask has become a necessity. However, masks may reduce the accuracy of face recognition to some degree. Thus, it is necessary to use deep learning to increase face recognition accuracy by recovering the face with a mask. For this purpose, this study proposed an AI-based model based on Pix2pix and U-net generator for restoring face mask images using the paired image database. In the training step, we used two adversarial models, including one generator and one discriminator. Then they are extended to a conditional model, which will be piped to the Pix2pix algorithm once again. U-Net was built in the training of the generator. The loss curves of generator and discriminators show that as iteration time increases, the loss of fake discriminator becomes lower stably. In contrast, the loss of real discriminator has the same tendency. In the meantime, the loss of generator shows an increased tendency. The result indicates that our model can help build reliable face mask restoration for daily use, which helps to improve the recognition accuracy of the face with a mask.
Full text:
Available
Collection:
Databases of international organizations
Database:
English Web of Science
Language:
English
Journal:
2nd International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)
Year:
2021
Document Type:
Article
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