Identity statement area
Reference TypeConference Proceedings
Last Update2006: administrator
Metadata Last Update2020: administrator
Citation KeyKitaniThomGill:2006:StDiMo
TitleA Statistical Discriminant Model for Face Interpretation and Reconstruction
Date8-11 Oct. 2006
Access Date2020, Dec. 03
Number of Files1
Size303 KiB
Context area
Author1 Kitani, Edson
2 Thomaz, Carlos
3 Gillies, Duncan
Affiliation1 Department of Electrical Engineering, Centro Universitário da FEI, São Paulo, Brazil
2 Department of Electrical Engineering, Centro Universitário da FEI, São Paulo, Brazil
3 Department of Computing, Imperial College, London, UK
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2006-07-07 19:03:30 :: cethomaz -> banon ::
2006-08-30 21:51:35 :: banon -> cethomaz ::
2008-07-17 14:11:02 :: cethomaz -> administrator ::
2009-08-13 20:38:00 :: administrator -> banon ::
2010-08-28 20:02:22 :: banon -> administrator ::
2020-02-19 03:17:34 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsStatistical discriminant model, face interpretation and reconstruction.
AbstractMultivariate statistical approaches have played an important role of recognising face images and charac-terizing their differences. In this paper, we introduce the idea of using a two-stage separating hyper-plane, here called Statistical Discriminant Model (SDM), to interpret and reconstruct face images. Analogously to the well-known Active Appearance Model proposed by Cootes et. al, SDM requires a previous alignment of all the images to a common template to minimise varia-tions that are not necessarily related to differences between the faces. However, instead of using landmarks or annotations on the images, SDM is based on the idea of using PCA to reduce the dimensionality of the original images and a maximum uncertainty linear classifier (MLDA) to characterise the most discrimi-nant changes between the groups of images. The experimental results based on frontal face images indicate that the SDM approach provides an intuitive interpretation of the differences between groups, reconstructing characteristics that are very subjective in human beings, such as beauty and happiness.
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