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Generic Facial Encoding for Shape Alignment with Active Models

  • William Ivaldi
  • Maurice Milgram
  • Stéphane Gentric
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)

Abstract

The modelisation of human faces from images can be done by the mean of morphable models such as AAMs. However, fitting such models without previous estimations is a challenging task. Shape estimation needs a close texture reference, and texture approximation requires shape knowledge. In this paper, we address the efficiency of sampling and generic encoding in regard to the shape alignment accuracy, without previous texture approximation. The hybrid method we propose is based on a relative barycentric resampling of the face model, a generic coding of the reference texture and a normalized cost function. We also present a new warping function definition to simplify the initial global parameter estimation. These new subsampling and encoding frameworks improve the accuracy of facial shape alignment in unconstrained cases.

Keywords

Cost Function Global Parameter Warping Function Active Appearance Model Reference Texture 
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 2006

Authors and Affiliations

  • William Ivaldi
    • 1
    • 2
  • Maurice Milgram
    • 1
  • Stéphane Gentric
    • 2
  1. 1.ISIR-PRCUniversité Pierre et Marie Curie, Paris-6Ivry/SeineFrance
  2. 2.SAGEM Défense Sécurité – Groupe SAFRANEragny/OiseFrance

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