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Robust parameterized component analysis: Theory and applications to {2D} facial appearance models




Principal component analysis (PCA) has been successfully applied to construct linear models of shape, graylevel, and motion in images. 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 consider 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, the main contribution of this paper is to 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 preliminary experiments relevant to applications including video-conferencing and avatar animation.

Author(s): De la Torre, F. and Black, M. J.
Journal: Computer Vision and Image Understanding
Volume: 91
Number (issue): 1-2
Pages: 53--71
Year: 2003
Month: July

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

Links: pdf


  title = {Robust parameterized component analysis: Theory and applications to {2D} facial appearance models},
  author = {De la Torre, F. and Black, M. J.},
  journal = {Computer Vision and Image Understanding},
  volume = {91},
  number = {1-2},
  pages = {53--71},
  month = jul,
  year = {2003},
  month_numeric = {7}