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Locating Facial Features with an Extended Active Shape Model

  • Stephen Milborrow
  • Fred Nicolls
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

We make some simple extensions to the Active Shape Model of Cootes et al. [4], and use it to locate features in frontal views of upright faces. We show on independent test data that with the extensions the Active Shape Model compares favorably with more sophisticated methods. The extensions are (i) fitting more landmarks than are actually needed (ii) selectively using two- instead of one-dimensional landmark templates (iii) adding noise to the training set (iv) relaxing the shape model where advantageous (v) trimming covariance matrices by setting most entries to zero, and (vi) stacking two Active Shape Models in series.

Keywords

Facial Feature Mahalanobis Distance Shape Model Kernel Principal Component Analysis 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 2008

Authors and Affiliations

  • Stephen Milborrow
    • 1
  • Fred Nicolls
    • 1
  1. 1.Department of Electrical EngineeringUniversity of Cape TownSouth Africa

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