A Model Based Approach for Expressions Invariant Face Recognition

  • Zahid Riaz
  • Christoph Mayer
  • Matthias Wimmer
  • Michael Beetz
  • Bernd Radig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


This paper describes an idea of recognizing the human face in the presence of strong facial expressions using model based approach. The features extracted for the face image sequences can be efficiently used for face recognition. The approach follows in 1) modeling an active appearance model (AAM) parameters for the face image, 2) using optical flow based temporal features for facial expression variations estimation, 3) and finally applying classifier for face recognition. The novelty lies not only in generation of appearance models which is obtained by fitting active shape model (ASM) to the face image using objective functions but also using a feature vector which is the combination of shape, texture and temporal parameters that is robust against facial expression variations. Experiments have been performed on Cohn-Kanade facial expression database using 62 subjects of the database with image sequences consisting of more than 4000 images. This achieved successful face recognition rate up to 91.17% using binary decision tree (BDT), 98.6% using Bayesian Networks (BN) with 10-fold cross validation in the presence of six different facial expressions.


Active Appearance Models Face Recognition Facial Expressions Recognition Binary Decision Trees Bayesian Classifier 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zahid Riaz
    • 1
  • Christoph Mayer
    • 1
  • Matthias Wimmer
    • 1
  • Michael Beetz
    • 1
  • Bernd Radig
    • 1
  1. 1.Department of InformaticsTechnische Universität MünchenGarchingGermany

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