phat 24(1):

Editorial

Masked GANs for Face Completion: A Novel Deep Learning Approach

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  • @ARTICLE{10.4108/eetpht.9.4850,
        author={Anshuman Sharma and Biswaroop Nath and Tejaswini Kar and D Khasim},
        title={Masked GANs for Face Completion: A Novel Deep Learning Approach},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={1},
        keywords={Generative Adversarial Networks, Masked data, face images},
        doi={10.4108/eetpht.9.4850}
    }
    
  • Anshuman Sharma
    Biswaroop Nath
    Tejaswini Kar
    D Khasim
    Year: 2024
    Masked GANs for Face Completion: A Novel Deep Learning Approach
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.4850
Anshuman Sharma1,*, Biswaroop Nath1, Tejaswini Kar1, D Khasim2
  • 1: KIIT University
  • 2: Vellore Institute of Technology University
*Contact email: anshumansharma179@gmail.com

Abstract

INTRODUCTION: Recent deep learning based image editing methods have achieved promising results for removing object in an image but fail to generate appreciable performance for removing large objects of complex nature, especially mask from facial images. Towards this goal the objective of this work is to remove mask objects in facial images. In this study, authors propose a novel approach for face completion using Generative Adversarial Networks (GANs) that utilize masked data. This technology can help in image restoration and preservation, thus enabling us to cherish those memories that are held dear to our hearts. OBJECTIVES: Train a GAN to learn the mapping from incomplete to complete face images by utilizing a masked input image. METHODS: The discriminator is trained to distinguish between face images and full ground truth images. Our results indicate that our technique generates high-quality, realistic facial images that are visually comparable to the ground truth and that it can generalise to new faces that were not encountered during training. RESULTS: Our findings indicate that GANs with masked inputs are a good approach for generating whole face images from partial or masked data. CONCLUSION: Our experimental findings show that our method produces facial images of great quality and realism that are visually equivalent to the actual thing. Our proposed approach can also be applied to fresh faces that weren’t seen. The performance can still be improved further using larger dataset. Also, further investigation into adversial attacks may help in improving performance. This technology can be further utilized for developing realtime mask removal software as well.