ORIGINAL RESEARCH article
Front. Neurol.
Sec. Neuro-Ophthalmology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1616509
This article is part of the Research TopicAdvances in Understanding Visual Disorders Linked to Cortical DysfunctionView all 8 articles
Scanning faces: A deep learning approach to studying the eye movements of prosopagnosic subjects
Provisionally accepted
- University of British Columbia, Vancouver, Canada
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Healthy individuals show fixation biases when scanning faces, likely towards the regions that are most informative for identifying faces. Some reports suggest that subjects with prosopagnosia, an impairment in face recognition, have anomalous face scanning.Objective: Our goal was to determine whether an approach using artificial intelligence could identify key scanning markers of prosopagnosia.We used an image-classification technique based on deep learning to study the fixations of subjects with and without prosopagnosia during a face recognition task. We identified the number of fixations that maximizes classification performance and developed two methods of displaying scanpaths as images, each used to train a convolutional neural network.Results: Optimal classification of acquired prosopagnosic from control trials required four fixations, with an AUC of 80%. The model showed a greater tendency to fixate the lower face and the right eye in acquired prosopagnosia. Optimal classification of developmental prosopagnosic from control trials required 16 fixations, with an AUC of 69%. Fixations on developmental prosopagnosic trials were shifted more towards peripheral regions. When the classifier trained to discriminate acquired prosopagnosia from controls was asked to analyze the developmental prosopagnosic trials, the latter were classified as being more like control scanpaths.Only a few fixations during face scanning are required to differentiate controls from acquired prosopagnosia, with the latter showing anomalous biases.Developmental prosopagnosic scanpaths resemble degraded control scanpaths rather than anomalous biases. This study shows the potential of deep learning to identify abnormal behavioral markers in a disorder of complex visual processing.
Keywords: face recognition, scanpath, artificial intelligence, Neural Network, Developmental
Received: 23 Apr 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Barton, Kazemian and Oruc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jason Jeremy Sinclair Barton, University of British Columbia, Vancouver, Canada
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.