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

Abstract

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.

A UNET++ and CoGAN-based method to remove face masks from the masked faces

“…It's worth highlighting that the technique advanced by Jiang et al [21], which hinges on a single generator and discriminator, bears striking resemblance to the framework of the suggested approach. Nonetheless, the results continue to underscore the superiority of the proposed method in terms of structural similarity and signal preservation over noise, surpassing other GAN-based methodologies when evaluated on the dataset employed within this study.…”

“…Subsequently, others followed similar approaches [18] [19] using the Pix2Pix GAN, generating datasets and employing binary segmentation in their modi ed models. Kumar et al [21] introduced a spatial attention module within the C-GAN framework for unmasking masked faces, yielding high-accuracy results.…”

“…This dataset encompasses a comprehensive collection of 12,355 images showcasing the top 100 Bollywood personalities. To enhance the dataset's realism, we also incorporated sample images from the dataset used by authors in [21], thus augmenting the dataset's diversity and mitigating the risk of neural networks converging to local minima. Figure 1 offers a glimpse of the proposed dataset's structure.…”