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A Survey of 3D Face Recognition Methods

  • Alize Scheenstra
  • Arnout Ruifrok
  • Remco C. Veltkamp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)

Abstract

Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction,facial expression, and aging. The main purpose of this overview is to describe the recent 3D face recognition algorithms. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren’t capable of handling facial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future.

Keywords

Facial Expression Face Recognition Recognition Rate Gesture Recognition Gabor Wavelet 
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 2005

Authors and Affiliations

  • Alize Scheenstra
    • 1
  • Arnout Ruifrok
    • 2
  • Remco C. Veltkamp
    • 1
  1. 1.Institute of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  2. 2.Netherlands Forensic InstituteDen HaagThe Netherlands

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