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Open Access

Facial Expression Biometrics Using Statistical Shape Models

  • Wei Quan1,
  • Bogdan J. Matuszewski1Email author,
  • Lik-Kwan Shark1 and
  • Djamel Ait-Boudaoud1
EURASIP Journal on Advances in Signal Processing20092009:261542

Received: 30 September 2008

Accepted: 18 August 2009

Published: 13 October 2009


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.


Information TechnologyFacial ExpressionSignificant FeatureQuantum InformationShape Variation

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Authors’ Affiliations

Applied Digital Signal and Image Processing Research Centre, University of Central Lancashire, Preston, UK


© 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.