Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

TL;DR

This paper introduces a new dataset and algorithm to improve fairness and accuracy in facial skin tone estimation by leveraging scene information to disambiguate lighting and albedo, addressing biases in existing methods.

Contribution

The authors present a novel evaluation dataset (FAIR) and an algorithm (TRUST) that utilize scene context to reduce racial bias in skin tone estimation.

Findings

TRUST significantly outperforms existing methods in accuracy.

The new dataset enables fairer evaluation across skin tones.

Scene-based disambiguation improves albedo estimation robustness.

Abstract

Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the appearance, represented by albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, albedo estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene -- as opposed to a cropped image of the face -- contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning both on the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state-of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code will be made available for research purposes at https://trust.is.tue.mpg.de.