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Generic Active Appearance Models Revisited

  • Georgios Tzimiropoulos
  • Joan Alabort-i-Medina
  • Stefanos Zafeiriou
  • Maja Pantic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

The proposed Active Orientation Models (AOMs) are generative models of facial shape and appearance. Their main differences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a different statistical model of appearance, (ii) they are accompanied by a robust algorithm for model fitting and parameter estimation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complexity. The project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm which is admittedly the fastest algorithm for fitting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outperforms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments ( http://ibug.doc.ic.ac.uk/resources ).

Keywords

Appearance Model Deformable Model Active Appearance Model Appearance Variation Face Alignment 
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 2013

Authors and Affiliations

  • Georgios Tzimiropoulos
    • 1
    • 2
  • Joan Alabort-i-Medina
    • 1
  • Stefanos Zafeiriou
    • 1
  • Maja Pantic
    • 1
    • 3
  1. 1.Department of ComputingImperial College LondonUnited Kingdom
  2. 2.School of Computer ScienceUniversity of LincolnUnited Kingdom
  3. 3.Faculty of Electrical Engineering, Mathematics and Computer ScienceUniversity of TwenteThe Netherlands

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