%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2006/07.07.19.03
%2 sid.inpe.br/sibgrapi@80/2006/07.07.19.03.30
%@doi 10.1109/SIBGRAPI.2006.3
%T A Statistical Discriminant Model for Face Interpretation and Reconstruction
%D 2006
%A Kitani, Edson,
%A Thomaz, Carlos,
%A Gillies, Duncan,
%@affiliation Department of Electrical Engineering, Centro Universitário da FEI, São Paulo, Brazil
%@affiliation Department of Electrical Engineering, Centro Universitário da FEI, São Paulo, Brazil
%@affiliation Department of Computing, Imperial College, London, UK
%E Oliveira Neto, Manuel Menezes de,
%E Carceroni, Rodrigo Lima,
%B Brazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
%C Manaus, AM, Brazil
%8 8-11 Oct. 2006
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Statistical discriminant model, face interpretation and reconstruction.
%X Multivariate 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.
%@language en
%3 thomaz-faces.pdf