Explicit Control over Race-Related Facial Phenotypes in Generative Models

Core Concepts

A novel GAN framework that enables fine-grained control over individual race-related facial phenotype attributes, such as skin color, hair color, and facial feature shapes, while preserving facial identity.

Abstract

The authors propose a GAN-based framework that achieves explicit control over race-related facial phenotypes by factorizing the latent space into components corresponding to specific attributes. The framework relies on 2D metric-based evaluations to quantify and disentangle attributes like skin color, hair color, nose shape, eye shape, and mouth shape, without requiring 3D data or manual annotations.
The key highlights of the approach are:

It introduces the CelebA-HQ-Augmented-Cleaned dataset, a semi-synthesized, manually-cleaned, high-quality dataset with diverse racial distribution, to address the imbalance in existing face datasets.

The proposed framework integrates the generator-discriminator architecture of StyleGAN2 and eliminates the need for 3D rendering parameters used in prior work.

Experiments show the framework achieves higher image quality and better controllability over race-related facial phenotypes compared to the baseline ConfigNet approach.

While the framework demonstrates strong control over color-based attributes like skin and hair color, it faces challenges in disentangling shape-based attributes like nose, eye, and mouth shapes due to their greater entanglement with facial identity.

The authors conclude that their work lays the foundation for creating controlled face image variations to mitigate racial bias in automated facial analysis tasks.

Stats

The authors use the following datasets for training and evaluation:

FFHQ dataset: 50,000 images for training, 10,000 for validation
CelebA-HQ dataset: 17,861 original images and 8,652 augmented images in the curated CelebA-HQ-Augmented-Cleaned dataset

Quotes

"Our framework factors the latent (feature) space into elements that correspond to race-related facial phenotype representations, thereby separating phenotype aspects (e.g. skin, hair colour, nose, eye, mouth shapes), which are notoriously difficult to annotate robustly in real-world facial data."
"Concurrently, we also introduce a high quality augmented, diverse 2D face image dataset drawn from CelebA-HQ for GAN training."

Key Insights Distilled From

Disentangling Racial Phenotypes

by Seyma Yucer,... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19897.pdf

Deeper Inquiries

How can the proposed framework be extended to better disentangle shape-based facial attributes like nose, eye, and mouth shapes

To better disentangle shape-based facial attributes like nose, eye, and mouth shapes, the proposed framework can be extended in several ways:

Enhanced Feature Representation Models: Implementing advanced feature representation models, such as visual transformers, can help capture intricate details and nuances in shape attributes. These models can be trained on manually generated patch imagery to improve the disentanglement of shape-related features.
Fine-tuning on Shape Attributes: By fine-tuning the network specifically on shape attributes like nose, eye, and mouth shapes, the model can learn to better separate these features from other identity-related factors. This targeted training can help in achieving more precise control over these attributes.
Augmented Training Data: Including a more diverse and balanced dataset that covers a wide range of nose, eye, and mouth shapes can help the model learn a more comprehensive representation of these features. Augmented data with variations in facial structures can aid in better disentangling shape attributes.

What other applications, beyond mitigating racial bias, could benefit from the fine-grained control over race-related facial phenotypes enabled by this framework

Beyond mitigating racial bias, the fine-grained control over race-related facial phenotypes enabled by this framework can benefit various applications:

Personalized Healthcare: Healthcare systems can utilize this technology for personalized patient care, where specific facial attributes related to health conditions can be analyzed and monitored with precision.
Entertainment Industry: Film and gaming industries can leverage this framework for creating diverse and realistic characters with controlled facial features, enhancing the overall visual experience for users.
Forensic Analysis: Law enforcement agencies can use this technology for facial analysis in criminal investigations, enabling accurate identification and analysis of suspects based on detailed facial attributes.
Virtual Try-On: Retail and fashion industries can implement this framework for virtual try-on applications, allowing customers to visualize products on avatars with customized facial features for a more personalized shopping experience.

How can the insights from this work be leveraged to develop more inclusive and equitable facial analysis systems that account for diverse human facial characteristics

The insights from this work can be instrumental in developing more inclusive and equitable facial analysis systems by:

Enhanced Representation: By incorporating a diverse range of facial characteristics in the training data, the systems can better account for the variability in human facial features, leading to more accurate and unbiased analysis results.
Bias Mitigation Strategies: The learnings from this framework can be used to develop robust bias mitigation strategies in facial analysis systems, ensuring fair and equitable treatment across different demographic groups.
User-Centric Design: Implementing user-centric design principles based on the insights gained can help in creating facial analysis systems that cater to the needs of diverse user populations, promoting inclusivity and accessibility.
Ethical Considerations: By considering ethical implications and potential biases in facial analysis, systems can be designed with built-in safeguards to prevent discriminatory outcomes and ensure fair treatment for all individuals.