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Facial Expression Biometrics Using Statistical Shape Models

  • Wei Quan
  • Bogdan J. Matuszewski
  • Lik-Kwan Shark
  • Djamel Ait-Boudaoud
Open Access
Research Article
Part of the following topical collections:
  1. Recent Advances in Biometric Systems: A Signal Processing Perspective

Abstract

This paper describes a novel method for representing different facial expressions based on the shape space vector (SSV) of the statistical shape model (SSM) built from 3D facial data. The method relies only on the 3D shape, with texture information not being used in any part of the algorithm, that makes it inherently invariant to changes in the background, illumination, and to some extent viewing angle variations. To evaluate the proposed method, two comprehensive 3D facial data sets have been used for the testing. The experimental results show that the SSV not only controls the shape variations but also captures the expressive characteristic of the faces and can be used as a significant feature for facial expression recognition. Finally the paper suggests improvements of the SSV discriminatory characteristics by using 3D facial sequences rather than 3D stills.

Keywords

Information Technology Facial Expression Significant Feature Quantum Information Shape Variation 
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

© Wei Quan et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Wei Quan
    • 1
  • Bogdan J. Matuszewski
    • 1
  • Lik-Kwan Shark
    • 1
  • Djamel Ait-Boudaoud
    • 1
  1. 1.Applied Digital Signal and Image Processing Research CentreUniversity of Central LancashirePrestonUK

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