Summary of E2f-net: Eyes-to-face Inpainting Via Stylegan Latent Space, by Ahmad Hassanpour et al.
E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space
by Ahmad Hassanpour, Fatemeh Jamalbafrani, Bian Yang, Kiran Raja, Raymond Veldhuis, Julian Fierrez
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Eyes-to-Face Network (E2F-Net) is a Generative Adversarial Network (GAN)-based model that inpaints facial images given the periocular region (eyes-to-face). This technique is crucial for applications like face recognition in occluded scenarios and image analysis with poor-quality captures. The E2F-Net extracts identity and non-identity features from the periocular region using two dedicated encoders, which are then mapped to the latent space of a pre-trained StyleGAN generator to leverage its performance and expressive capabilities. A new optimization technique is used to find the optimal code in the latent space for GAN inversion. The proposed method requires minimal training process, reducing computational complexity as a secondary benefit. Experimental results demonstrate that E2F-Net successfully reconstructs the whole face with high quality, outperforming current techniques despite less training and supervision efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to restore missing or damaged facial images called eyes-to-face inpainting. It’s like filling in the blanks of a puzzle to reveal the original image. The authors use a special type of artificial intelligence called Generative Adversarial Networks (GANs) to do this. They take the area around the eyes and map it to a larger space where they can manipulate the features to make them look more realistic. This method requires less training than other techniques, making it faster and more efficient. The results show that their method is able to reconstruct facial images with high quality. |
Keywords
» Artificial intelligence » Face recognition » Gan » Generative adversarial network » Latent space » Optimization
