Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives.

ABSTRACT: Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered b

SUBMITTER: Schwarz CG 

PROVIDER: S-EPMC8154695 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature