The CHROMA-FIT Dataset: Characterizing Human Ranges of Melanin for Increased Tone-Awareness

Gabriella Pangelinan, Xavier Merino, Samuel Langborgh, Kushal Vangara, Joyce Annan, Audison Beaubrun, Troy Weekes, Michael C. King; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 1170-1178

The disparate performance of face analytics technology across demographic groups is a well-documented phenomenon. In particular, these systems tend toward lower accuracy for darker-skinned individuals. Prior research exploring this asymmetry has largely relied on discrete race categories, but such labels are increasingly deemed insufficient to describe the wide range of human phenotypical features. Skin tone is a more objective measure, but there is a dearth of reliable skin tone-related image data. Existing tone annotations are derived from the images alone, either by human reviewers or automated processes. However, without ground-truth skin tone measurements from the subjects of the images themselves, there is no way to assess the consistency or accuracy of post-hoc methods. In this work, we present CHROMA-FIT, the first publicly available dataset of face images and corresponding ground-truth skin tone measurements. Our goal is to provide a baseline for tone-labeling methods in assessing and improving their accuracy. The dataset comprises approximately 2,300 still images of 209 participants in indoor and outdoor collection environments.