Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between same-labeled faces. To address these issues, we present A View From Somewhere (AVFS) -- a dataset of 638,180 human judgments of face similarity. We demonstrate the utility of AVFS for learning a continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under a novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human-interpretable decomposition of the dimensions used in the human-decision making process, and the importance distinct annotators place on each dimension. We additionally show the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.
@article{arxiv.2303.17176,
title = {A View From Somewhere: Human-Centric Face Representations},
author = {Jerone T. A. Andrews and Przemyslaw Joniak and Alice Xiang},
journal= {arXiv preprint arXiv:2303.17176},
year = {2023}
}
Comments
Accepted to ICLR 2023. Code and data may be found at https://github.com/SonyAI/a_view_from_somewhere