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Meticulously Detailed Eye Region Model

  • Tsuyoshi Moriyama
  • Takeo Kanade
  • Jing Xiao
  • Jeffrey F. Cohn
Part of the Signals and Commmunication Technologies book series (SCT)

Abstract

Automated analysis of facial images has found eyes still to be a difficult target [90, 96, 97, 125, 215, 221, 229, 230, 248, 360, 509, 692, 709]. The difficulty comes from the diversities in the appearance of eyes due to both structural individuality and motion of eyes, as shown in Fig. 2.1. Past studies have failed to represent these diversities adequately. For example, Tian et al. [616] used a pair of parabolic curves and a circle as a generic eye model, but parabolic curves have too few parameters to represent the complexity of eyelid shape and motion. Statistical models have been deployed to represent such individual differences for the whole eye region [322, 569, 635], but not for subregions, such as the eyelids, due in part to limited variation in training samples.

Keywords

Tracking Error Lower Eyelid Appearance Change Initial Frame Outer Corner 
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

  • Tsuyoshi Moriyama
    • 1
  • Takeo Kanade
    • 2
  • Jing Xiao
    • 3
  • Jeffrey F. Cohn
    • 2
  1. 1.Departement of Media and Image TechnologyTokyo Politechnic UniversityTokyoJapan
  2. 2.Carnegie Mellon UniversityPittsburghUSA
  3. 3.Institute of Biomedical Engineering, Epson Research an DevelopmentImperial College LondonLondonUK

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