Facial identity across the lifespan
dc.contributor.author | Mileva, Mila | |
dc.contributor.author | Young, AW | |
dc.contributor.author | Jenkins, R | |
dc.contributor.author | Burton, AM | |
dc.date.accessioned | 2020-04-03T09:26:47Z | |
dc.date.available | 2020-04-03T09:26:47Z | |
dc.date.issued | 2020-02 | |
dc.identifier.issn | 0010-0285 | |
dc.identifier.issn | 1095-5623 | |
dc.identifier.other | 101260 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/15486 | |
dc.description.abstract |
We can recognise people that we know across their lifespan. We see family members age, and we can recognise celebrities across long careers. How is this possible, despite the very large facial changes that occur as people get older? Here we analyse the statistical properties of faces as they age, sampling photos of the same people from their 20s to their 70s. Across a number of simulations, we observe that individuals' faces retain some idiosyncratic physical properties across the adult lifespan that can be used to support moderate levels of age-independent recognition. However, we found that models based exclusively on image-similarity only achieved limited success in recognising faces across age. In contrast, more robust recognition was achieved with the introduction of a minimal top-down familiarisation procedure. Such models can incorporate the within-person variability associated with a particular individual to show a surprisingly high level of generalisation, even across the lifespan. The analysis of this variability reveals a powerful statistical tool for understanding recognition, and demonstrates how visual representations may support operations typically thought to require conceptual properties. | |
dc.format.extent | 0-0 | |
dc.format.medium | Print-Electronic | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.subject | Face perception | |
dc.subject | Face recognition | |
dc.subject | Age perception | |
dc.title | Facial identity across the lifespan | |
dc.type | journal-article | |
dc.type | Journal Article | |
dc.type | Research Support, Non-U.S. Gov't | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000509629900003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.volume | 116 | |
plymouth.publication-status | Published | |
plymouth.journal | Cognitive Psychology | |
dc.identifier.doi | 10.1016/j.cogpsych.2019.101260 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Health | |
plymouth.organisational-group | /Plymouth/Faculty of Health/School of Psychology | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA04 Psychology, Psychiatry and Neuroscience | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA04 Psychology, Psychiatry and Neuroscience/UoA04 Psychology, Psychiatry and Neuroscience MANUAL | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.publisher.place | Netherlands | |
dcterms.dateAccepted | 2019-11-30 | |
dc.rights.embargodate | 9999-12-31 | |
dc.identifier.eissn | 1095-5623 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1016/j.cogpsych.2019.101260 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2020-02 | |
rioxxterms.type | Journal Article/Review |