Close
Metadata

@InProceedings{KitaniThomGill:2006:StDiMo,
               author = "Kitani, Edson and Thomaz, Carlos and Gillies, Duncan",
          affiliation = "Department of Electrical Engineering, Centro Universit{\'a}rio da 
                         FEI, S{\~a}o Paulo, Brazil and Department of Electrical 
                         Engineering, Centro Universit{\'a}rio da FEI, S{\~a}o Paulo, 
                         Brazil and Department of Computing, Imperial College, London, UK",
                title = "A Statistical Discriminant Model for Face Interpretation and 
                         Reconstruction",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Statistical discriminant model, face interpretation and 
                         reconstruction.",
             abstract = "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.",
  conference-location = "Manaus",
      conference-year = "8-11 Oct. 2006",
             language = "en",
           targetfile = "thomaz-faces.pdf",
        urlaccessdate = "2020, Nov. 28"
}


Close