Contribution of shape and surface reflectance information to kinship detection in 3D face images

Fasolt, Vanessa; Holzleitner, Iris J.; Lee, Anthony J.; O'Shea, Kieran J.; DeBruine, Lisa M.

Abstract

Previous research has established that humans are able to detect kinship among strangers from facial images alone. The current study investigated what facial information is used for making those kinship judgments, specifically the contribution of face shape and surface reflectance information (e.g., skin texture, tone, eye and eyebrow color). Using 3D facial images, 195 participants were asked to judge the relatedness of 100 child pairs, half of which were related and half of which were unrelated. Participants were randomly assigned to judge one of three stimulus versions: face images with both surface reflectance and shape information present (reflectance and shape version), face images with shape information removed but surface reflectance present (reflectance version), or face images with surface reflectance information removed but shape present (shape version). Using binomial logistic mixed models, we found that participants were able to detect relatedness at levels above chance for all three stimulus versions. Overall, both individual shape and surface reflectance information contribute to kinship detection, and both cues are optimally combined when presented together. Preprint, preregistration, code, and data are available on the Open Science Framework (osf.io/7ftxd).

• Journal Article Type: Article
• Acceptance Date: Aug 1, 2019
• Publication Date: Oct 17, 2019
• Deposit Date: Jun 11, 2021
• Publicly Available Date: Jun 11, 2021
• Journal: Journal of Vision
• Electronic ISSN: 1534-7362
• Publisher: Association for Research in Vision and Ophthalmology
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 12
• Article Number: 9
• DOI: https://doi.org/10.1167/19.12.9
• Public URL: https://uwe-repository.worktribe.com/output/7460752