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. 2020 Mar 10;10:4443. doi: 10.1038/s41598-020-61333-3

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Study design. Three images of each participant were taken with the 3dMDface (left) and the Vectra H1 (right), for a total of 6 image replicates per participant. Each image was then registered three times with MeshMonk, resulting in a total of 18 quasi-landmark configurations per individual. Comparisons of each set of registrations to the average of all three registration iterations (column-wise) led to estimates of MeshMonk precision. Within-camera comparisons of the three average registrations to the overall average for that camera gave estimates of participant error per camera as well as technical error per camera, when analyzing the mannequin images. Lastly, the average quasi-landmark configuration from each camera was compared to create estimates of error due to imaging system. Individual imaged is one of the authors.