Caricaturing faces to improve identity recognition in low vision simulations: How effective is current-generation automatic assignment of landmark points?
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McKone, Elinor; Robbins, Rachel; He, Xuming; Barnes, Nick
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Purpose: Previous behavioural studies demonstrate that face caricaturing can provide an effective image enhancement method for improving poor face identity perception in low vision simulations (e.g., age-related macular degeneration, bionic eye). To translate caricaturing usefully to patients, assignment of the multiple face landmark points needed to produce the caricatures needs to be fully automatised. Recent development in computer science allows automatic face landmark detection of 68...[Show more]
dc.contributor.author | McKone, Elinor | |
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dc.contributor.author | Robbins, Rachel | |
dc.contributor.author | He, Xuming | |
dc.contributor.author | Barnes, Nick | |
dc.date.accessioned | 2020-12-22T05:08:56Z | |
dc.date.available | 2020-12-22T05:08:56Z | |
dc.identifier.citation | : McKone E, Robbins RA, He X, Barnes N (2018) Caricaturing faces to improve identity recognition in low vision simulations: How effective is current-generation automatic assignment of landmark points? PLoS ONE 13(10): e0204361. https://doi.org/10.1371/journal.pone.0204361 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.uri | http://hdl.handle.net/1885/219017 | |
dc.description.abstract | Purpose: Previous behavioural studies demonstrate that face caricaturing can provide an effective image enhancement method for improving poor face identity perception in low vision simulations (e.g., age-related macular degeneration, bionic eye). To translate caricaturing usefully to patients, assignment of the multiple face landmark points needed to produce the caricatures needs to be fully automatised. Recent development in computer science allows automatic face landmark detection of 68 points in real time and in multiple viewpoints. However, previous demonstrations of the behavioural effectiveness of caricaturing have used higherprecision caricatures with 147 landmark points per face, assigned by hand. Here, we test the effectiveness of the auto-assigned 68-point caricatures. We also compare this to the hand-assigned 147-point caricatures. Method: We assessed human perception of how different in identity pairs of faces appear, when veridical (uncaricatured), caricatured with 68-points, and caricatured with 147-points. Across two experiments, we tested two types of low-vision images: a simulation of blur, as experienced in macular degeneration (testing two blur levels); and a simulation of the phosphenised images seen in prosthetic vision (at three resolutions). Results: The 68-point caricatures produced significant improvements in identity discrimination relative to veridical. They were approximately 50% as effective as the 147-point caricatures. Conclusion: Realistic translation to patients (e.g., via real time caricaturing with the enhanced signal sent to smart glasses or visual prosthetic) is approaching feasibility. For maximum effectiveness software needs to be able to assign landmark points tracing out all details of feature and face shape, to produce high-precision caricatures. | |
dc.description.sponsorship | This work was funded by the Australian Research Council (http://www.arc.gov.au/) grant DP150100684 to EM and National Health and Medical Research Council (https://www.nhmrc.gov.au/) 1082358 to NB. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_AU | |
dc.publisher | Public Library of Science | |
dc.rights | © 2018 McKone et al. | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | PLOS ONE (Public Library of Science) | |
dc.title | Caricaturing faces to improve identity recognition in low vision simulations: How effective is current-generation automatic assignment of landmark points? | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 13 | |
dcterms.dateAccepted | 2018-09-05 | |
dc.date.issued | 2018-10-04 | |
local.identifier.absfor | 170112 - Sensory Processes, Perception and Performance | |
local.identifier.ariespublication | u4485658xPUB1317 | |
local.publisher.url | https://journals.plos.org/ | |
local.type.status | Published Version | |
local.contributor.affiliation | McKone, Elinor, College of Health and Medicine, ANU | |
local.contributor.affiliation | Robbins, Rachel, College of Health and Medicine, ANU | |
local.contributor.affiliation | He, Xuming, ShanghaiTech University | |
local.contributor.affiliation | Barnes, Nicholas (Nick), College of Engineering and Computer Science, ANU | |
dc.relation | http://purl.org/au-research/grants/arc/DP150100684 | |
dc.relation | http://purl.org/au-research/grants/nhmrc/1082358 | |
local.bibliographicCitation.issue | 10 | |
local.bibliographicCitation.startpage | 1 | |
local.bibliographicCitation.lastpage | 18 | |
local.identifier.doi | 10.1371/journal.pone.0204361 | |
local.identifier.absseo | 970117 - Expanding Knowledge in Psychology and Cognitive Sciences | |
dc.date.updated | 2020-09-13T08:20:12Z | |
local.identifier.scopusID | 2-s2.0-85054423045 | |
dcterms.accessRights | Open Access | |
dc.provenance | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
dc.rights.license | Creative Commons Attribution License | |
Collections | ANU Research Publications |
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