Achieving an effective fine-grained appearance variation over 2D facial
images, whilst preserving facial identity, is a challenging task due to the
high complexity and entanglement of common 2D facial feature encoding spaces.
Despite these challenges, such fine-grained control, by way of disentanglement
is a crucial enabler for data-driven racial bias mitigation strategies across
multiple automated facial analysis tasks, as it allows to analyse, characterise
and synthesise human facial diversity. In this paper, we propose a novel GAN
framework to enable fine-grained control over individual race-related phenotype
attributes of the facial images. 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.
Unlike prior work, our framework only relies upon 2D imagery and related
parameters to achieve state-of-the-art individual control over race-related
phenotype attributes with improved photo-realistic output.

本研究论文提出一种新颖的生成对抗网络（GAN）框架，以实现对人脸图像中与种族相关表型属性的细粒度控制，并提供改进的逼真输出。