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dc.contributor.authorHunter, David William
dc.contributor.authorTiddeman, Bernard Paul
dc.contributor.authorPerrett, David Ian
dc.identifier.citationHunter , D W , Tiddeman , B P & Perrett , D I 2012 , ' A genetic algorithm for face fitting ' , Paper presented at GRAPP - International Conference on Computer Graphics Theory and Applications , Rome, Italy , United Kingdom , 24/02/12 - 26/02/12 pp. 115-120 .
dc.identifier.otherPURE: 20248043
dc.identifier.otherPURE UUID: 418b9dc7-0654-4967-9732-6a89ddc25867
dc.identifier.otherScopus: 84862218570
dc.identifier.otherORCID: /0000-0002-6025-0939/work/64360959
dc.description.abstractAccurate estimation of the shape of human faces has many applications from computer-imaging to psychological research. One well known method is to fit a Three Dimensional Morphable Model to a target image. This method is attractive as the faces it constructs are already projected onto an orthogonal basis making further manipulation and analysis easier. So far its use in these fields has been limited the inaccuracy and inconvenience of current face-fitting methods. We present a method based on Genetic Algorithms that avoid the local minima and gradient image errors that current methods suffer from. It has the added advantage of requiring no manual interaction to initialise or guide the fitting process.
dc.rights©2012. SCITEPRESS Lda. This is the accepted manuscript of a Conference Paper originally submitted to the International Conference on Computer Graphics Theory and Applications (GRAPP 2012) available from
dc.subjectGenetic algorithmen
dc.subjectFace recognition modelsen
dc.subjectThree dimensional morphable modelsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleA genetic algorithm for face fittingen
dc.typeConference paperen
dc.contributor.institutionUniversity of St Andrews. School of Psychology and Neuroscienceen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Centre for Social Learning & Cognitive Evolutionen
dc.description.statusPeer revieweden

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