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GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal by Mohamed Mahmoud; Hyun-Soo Kang is a Engineering article available to read on EtoBox.

What is GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal about?

Deep-learning-based image inpainting methods have made remarkable advancements, particularly in object removal tasks. The removal of face masks has gained significant attention, especially in the wake of the COVID-19 pandemic, and while numerous methods have successfully addressed the removal of small objects, removing large and complex masks from faces remains demanding. This paper presents a novel two-stage network for unmasking faces considering the intricate facial features typically concealed by masks, such as noses, mouths, and chins. Additionally, the scarcity of paired datasets comprising masked and unmasked face images poses an additional challenge. In the first stage of our proposed model, we employ an autoencoder-based network for binary segmentation of the face mask. Subsequently, in the second stage, we introduce a generative adversarial network (GAN)-based network enhanced with attention and Masked–Unmasked Region Fusion (MURF) mechanisms to focus on the masked region. Our network generates realistic and accurate unmasked faces that resemble the original faces. We train our model on paired unmasked and masked face images sourced from CelebA, a large public dataset, an

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Author

Mohamed Mahmoud; Hyun-Soo Kang

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)