Face Alignment Using Boosting and Evolutionary Search

  • Hua Zhang
  • Duanduan Liu
  • Mannes Poel
  • Anton Nijholt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5995)

Abstract

In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.

Keywords

face alignment boosting appearance models granular features evolutionary search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hua Zhang
    • 1
  • Duanduan Liu
    • 2
  • Mannes Poel
    • 3
  • Anton Nijholt
    • 3
  1. 1.College of Software EngineeringSoutheast UniversityNanjingChina
  2. 2.Lab of Science and TechnologySoutheast UniversityNanjingChina
  3. 3.Human Media InteractionUniversity of TwenteEnschedeThe Netherlands

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