E2F-Net: Eyes-to-Face Inpainting via StyleGAN

Konsep Inti

Proposing E2F-Net for high-quality face inpainting using periocular region with minimal training and state-of-the-art StyleGAN.

Abstrak

The content discusses the development of E2F-Net, a Generative Adversarial Network (GAN)-based model for face inpainting focusing on the periocular region. The method aims to reconstruct faces with high quality, preserving identity features. Extensive experiments show superior results compared to existing techniques, utilizing pre-trained networks and optimization for GAN inversion. Seven datasets are generated for training and validation purposes.

Introduction

Face inpainting is crucial for applications like face recognition in occluded scenarios.
Challenges include photometric, geometric complexities, and preserving identity characteristics.

Methodology

Utilizes two encoders to extract identity and non-identity features from the periocular region.
Maps features to latent space of pre-trained StyleGAN generator.

Results

Outperforms current techniques in reconstructing faces with realistic hair regions.
Successfully preserves ID and non-ID traits with minimum supervision.

Training and Losses

Includes various loss functions such as perceptual loss, style loss, identity loss, landmark loss, reconstruction loss, and adversarial loss.

Experiments

Evaluation conducted on seven generated datasets including E2F-StyleGANdb, E2F-CelebA-HQ, E2F-FFHQ among others.

Comparison Methods

Compared against PIC, EC, LaFIn, and E2F-GAN using statistical metrics like ℓ1 loss, PSNR, SSIM, FID, TV along with identity metrics like FNMR.

Statistik

50%の精度を達成しました。
生成された画像は256×256にリサイズされました。

Kutipan

"Among all facial elements, the eyes are one of the most expressive organs on the human face."
"The proposed solution should preserve the identity-related features present in the eyes region when reconstructing the whole face."

Wawasan Utama Disaring Dari

E2F-Net

by Ahmad Hassan... pada arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12197.pdf

Pertanyaan yang Lebih Dalam

How can the concept of eyes-to-face inpainting be applied in other areas beyond computer vision

Eyes-to-face inpainting, as a concept, can be applied beyond computer vision in various fields. One potential application is in the field of digital art and entertainment. Artists and designers could use this technology to enhance or restore facial features in images for creative projects. Additionally, in the medical field, this technology could be utilized for reconstructive surgery planning by inpainting missing facial features based on existing ones. In forensic science, eyes-to-face inpainting could aid in generating realistic facial reconstructions from partial remains or skeletal structures.

What are potential drawbacks or limitations of relying heavily on pre-trained models like StyleGAN

Relying heavily on pre-trained models like StyleGAN may have some drawbacks and limitations. One limitation is the lack of flexibility and adaptability to new datasets or specific requirements. Pre-trained models are trained on specific datasets with certain characteristics, which may not always align perfectly with the data at hand. Another drawback is the risk of bias inherent in pre-trained models due to biases present in the training data. This bias can lead to inaccurate or skewed results when used on diverse datasets or populations.

How might advancements in face inpainting technology impact privacy concerns related to facial recognition systems

Advancements in face inpainting technology can have significant implications for privacy concerns related to facial recognition systems. As face inpainting techniques improve and become more sophisticated, there is a higher risk of malicious actors using these technologies for identity theft or impersonation purposes. For instance, high-quality inpainted faces could potentially fool facial recognition systems leading to unauthorized access or fraudulent activities. Additionally, there are concerns about privacy violations if sensitive information such as personal details or emotions are inferred from incomplete faces through advanced inpainting methods without consent from individuals involved.