Summary of Disentangling Racial Phenotypes: Fine-grained Control Of Race-related Facial Phenotype Characteristics, by Seyma Yucer et al.
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
by Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed, Toby P. Breckon
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel GAN framework enables fine-grained control over individual race-related phenotype attributes of facial images. The framework disentangles the latent space into elements that correspond to race-related facial representations, separating aspects like skin tone, hair color, nose shape, eye shape, and mouth shape. This allows for robust annotation in real-world data. To train the GAN, a high-quality augmented dataset is introduced, drawn from CelebA-HQ. The framework relies solely on 2D imagery to achieve state-of-the-art individual control over race-related attributes with improved photo-realistic output. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to control facial features in pictures. They want to be able to change specific traits like skin tone or hair color without changing the person’s identity. This is important because it can help reduce biases in facial recognition technology. The team proposes a new method using artificial intelligence and creates a large dataset of diverse faces to train their system. Their approach only uses 2D images and doesn’t require any special annotations. |
Keywords
» Artificial intelligence » Gan » Latent space