Robust Expression-Invariant Face Recognition from Partially Missing Data

  • Alexander M. Bronstein
  • Michael M. Bronstein
  • Ron Kimmel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)


Recent studies on three-dimensional face recognition proposed to model facial expressions as isometries of the facial surface. Based on this model, expression-invariant signatures of the face were constructed by means of approximate isometric embedding into flat spaces. Here, we apply a new method for measuring isometry-invariant similarity between faces by embedding one facial surface into another. We demonstrate that our approach has several significant advantages, one of which is the ability to handle partially missing data. Promising face recognition results are obtained in numerical experiments even when the facial surfaces are severely occluded.


Facial Expression Face Recognition Geodesic Distance Iterative Close Point Isometric Embedding 
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

  • Alexander M. Bronstein
    • 1
  • Michael M. Bronstein
    • 1
  • Ron Kimmel
    • 1
  1. 1.Dept. of Computer ScienceTechnion – Israel Institute of TechnologyHaifaIsrael

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