Dynamic 3D Facial Expression Recognition Using Robust Shape Features

  • Ahmed Maalej
  • Hedi Tabia
  • Halim Benhabiles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


In this paper we present a novel approach for dynamic facial expression recognition based on 3D geometric facial features. Geodesic distances between corresponding 3D open curves are computed and used as features to describe the facial changes across sequences of 3D face scans. Hidden Markov Models (HMMs) are exploited to learn the curves shape variation through a 3D frame sequences, and the trained models are used to classify six prototypic facial expressions. Our approach shows high performance, and an overall recognition rate of 94.45% is attained after a validation on the BU-4DFE database.


Facial expression recognition shape analysis hidden Markov models 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ahmed Maalej
    • 1
  • Hedi Tabia
    • 2
  • Halim Benhabiles
    • 3
  1. 1.LSIS/ENSAMArts et Mtiers ParisTechLilleFrance
  2. 2.ETIS/ENSEAUniversity of Cergy-Pontoise, CNRS, UMR 8051France
  3. 3.ESIGELEC/IRSEEM (EA 4353)Saint-Etienne du RouvrayFrance

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