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Combining Elastic and Statistical Models of Appearance Variation

  • T. F. Cootes
  • C. J. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1842)

Abstract

We propose a model of appearance and a matching method which combines ‘global’ models (in which a few parameters control global appearance) with local elastic or optical-flow-based methods, in which deformation is described by many local parameters together with some regularisation constraints. We use an Active Appearance Model (AAM) as the global model, which can match a statistical model of appearance to a new image rapidly. However, the amount of variation allowed is constrained by the modes of the model, which may be too restrictive (for instance when insufficient training examples are available, or the number of modes is deliberately truncated for effciency or memory conservation). To compensate for this, after global AAM convergence, we allow further local model deformation, driven by local AAMs around each model node. This is analogous to optical flow or ‘demon’ methods of non-linear image registration. We describe the technique in detail, and demonstrate that allowing this extra freedom can improve the accuracy of object location with only a modest increase in search time. We show the combined method is more accurate than either pure local or pure global model search.

Keywords

Face Image Image Registration Local Deformation Model Point 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 2000

Authors and Affiliations

  • T. F. Cootes
    • 1
  • C. J. Taylor
    • 1
  1. 1.Department of Imaging Science and Biomedical EngineeringUniversity of ManchesterManchesterUK

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