Copula Eigenfaces with Attributes: Semiparametric Principal Component Analysis for a Combined Color, Shape and Attribute Model

  • Bernhard Egger
  • Dinu Kaufmann
  • Sandro Schönborn
  • Volker Roth
  • Thomas Vetter
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 693)

Abstract

Principal component analysis is a ubiquitous method in parametric appearance modeling for describing dependency and variance in datasets. The method requires the observed data to be Gaussian-distributed. We show that this requirement is not fulfilled in the context of analysis and synthesis of facial appearance. The model mismatch leads to unnatural artifacts which are severe to human perception. As a remedy, we use a semiparametric Gaussian copula model, where dependency and variance are modeled separately. This model enables us to use arbitrary Gaussian and non-Gaussian marginal distributions. Moreover, facial color, shape and continuous or categorical attributes can be analyzed in an unified way. Accounting for the joint dependency between all modalities leads to a more specific face model. In practice, the proposed model can enhance performance of principal component analysis in existing pipelines: The steps for analysis and synthesis can be implemented as convenient pre- and post-processing steps.

Keywords

Copula Component Analysis Gaussian copula Principal component analysis Parametric Appearance Models 3D Morphable Model Face modeling Face synthesis Attributes 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bernhard Egger
    • 1
  • Dinu Kaufmann
    • 1
  • Sandro Schönborn
    • 1
  • Volker Roth
    • 1
  • Thomas Vetter
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of BaselBaselSwitzerland

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