Abstract
In 2020 and beyond, the opportunities to communicate with others while wearing a face mask have increased. A mask hides the mouth and facial muscles, making it difficult to convey facial expressions to others. In this study, we propose to use generative adversarial networks (GAN) to complete the facial region hidden by the mask. We defined custom loss functions that focus on the errors of the feature point coordinates of the face and the pixels in the masked region. As a result, we were able to generate images with higher quality than existing methods.
| Original language | English |
|---|---|
| Pages (from-to) | 125-133 |
| Number of pages | 9 |
| Journal | Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Volume | 5 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022 - Virtual, Online Duration: 2022 Feb 6 → 2022 Feb 8 |
Keywords
- COVID-19
- Face Completion
- Face Mask
- Generative Adversarial Networks
- Inpainting
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
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