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Robust Parameterized Component Analysis

Theory and Applications to 2D Facial Modeling
  • Fernando De la Torre
  • Michael J. Black
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)

Abstract

Principal Component Analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion. In particular, PCA has been widely used to model the variation in the appearance of people’s faces. We extend previous work on facial modeling for tracking faces in video sequences as they undergo significant changes due to facial expressions. Here we develop person-specific facial appearance models (PSFAM), which use modular PCA to model complex intra-person appearance changes. Such models require aligned visual training data; in previous work, this has involved a time consuming and error-prone hand alignment and cropping process. Instead, we introduce parameterized component analysis to learn a subspace that is invariant to affine (or higher order) geometric transformations. The automatic learning of a PSFAM given a training image sequence is posed as a continuous optimization problem and is solved with a mixture of stochastic and deterministic techniques achieving sub-pixel accuracy. We illustrate the use of the 2D PSFAM model with several applications including video-conferencing, realistic avatar animation and eye tracking.

Keywords

Training Image Motion Parameter Appearance Model Geometric Transformation Facial Animation 
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 2002

Authors and Affiliations

  • Fernando De la Torre
    • 1
  • Michael J. Black
    • 2
  1. 1.Department of Communications and Signal Theory, La Salle School of EngineeringUniversitat Ramon LLullBarcelonaSpain
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA

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