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Approximating 3D Facial Shape from Photographs Using Coupled Statistical Models

  • Mario Castelán
  • William A. P. Smith
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

In this paper we focus on the problem of developing a coupled statistical model that can be used to recover surface height from frontal photographs of faces. The idea is to couple intensity and height by jointly modeling their combined variations. We perform Principal Component Analysis (PCA) on the shape coefficients for both intensity and height training data in order to construct the coupled statistical model. Using the best-fit coefficients of an intensity image, height information can be implicitly recovered through the coupled statistical model. Experiments show that the method can generate good approximations of the facial surface shape from out-of-training photographs of faces.

Keywords

Face Recognition Couple Model Input Image Sample Covariance Matrix Active Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mario Castelán
    • 1
  • William A. P. Smith
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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