TRUST: Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Haiwen Feng, Timo Bolkart, Joachim Tesch, Michael J. Black and Victoria Abrevaya
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 albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance 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 on both 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 are available for research purposes on our
GitHub repo.
Paper
Video Introduction
FAIR Challenge
Check our newly released FAIR Challenge and benchmark dataset on the FAIR Challenge page.
TRUST implementation
Apr20, 2023
We are releasing the BalanceAlb albedo model and associated TRUST model (paper version)! Check it out :)
Jul19, 2022
TRUST code is available on GitHub.
Citation
If you find our work (codes, model, and dataset) useful to your research, please consider citing:
@inproceedings{Feng:TRUST:ECCV2022,
title = {Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation},
author = {Feng, Haiwen and Bolkart, Timo and Tesch, Joachim and Black, Michael J. and Abrevaya, Victoria},
booktitle = {European Conference on Computer Vision},
year = {2022}
}
This work was partly supported by the German Federal Ministry of Education and Research (BMBF): Tuebingen AI Center, FKZ: 01IS18039B