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

Fasolt, V., Holzleitner, I. J. , Lee, A. J. , O'Shea, K. J. and Debruine, L. M. (2019) Contribution of shape and surface reflectance information to kinship detection in 3D face images. Journal of Vision, 19(12), 9. (doi: 10.1167/19.12.9) (PMID:31621819)

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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 (

Item Type:Articles
Glasgow Author(s) Enlighten ID:DeBruine, Professor Lisa and Fasolt, Vanessa and Lee, Dr Anthony and O'Shea, Dr Kieran and Holzleitner, Dr Iris
Authors: Fasolt, V., Holzleitner, I. J., Lee, A. J., O'Shea, K. J., and Debruine, L. M.
College/School:College of Medical Veterinary and Life Sciences
College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Journal of Vision
Publisher:Association for Research in Vision and Ophthalmology
ISSN (Online):1534-7362
Published Online:17 October 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Journal of Vision 19(12):9
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
672531KINSHIP: How do humans recognise kin?Lisa DebruineEuropean Research Council (ERC)647910RI NEUROSCIENCE & PSYCHOLOGY