SAM C-GAN: a method for removal of face masks from masked faces.

Publication date: May 26, 2023

The past years of COVID-19 have attracted researchers to carry out benchmark work in face mask detection. However, the existing work does not focus on the problem of reconstructing the face area behind the mask and completing the face that can be used for face recognition. In order to address this problem, in this work we have proposed a spatial attention module-based conditional generative adversarial network method that can generate plausible images of faces without masks by removing the face masks from the face region. The method proposed in this work utilizes a self-created dataset consisting of faces with three types of face masks for training and testing purposes. With the proposed method, an SSIM value of 0. 91231 which is 3. 89% higher and a PSNR value of 30. 9879 which is 3. 17% higher has been obtained as compared to the vanilla C-GAN method.

Concepts Keywords
Covid C-GAN
Informatics Image editing
Mask Image translation
Reconstructing Object removal
Vanilla Spatial attention module


Type Source Name
drug DRUGBANK Ademetionine
disease MESH COVID-19
pathway REACTOME Translation
disease IDO object

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