Component-Based Face Recognition with 3D Morphable Models

  • Jennifer Huang
  • Bernd Heisele
  • Volker Blanz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


We present a novel approach to pose and illumination invariant face recognition that combines two recent advances in the computer vision field: component-based recognition and 3D morphable models. First, a 3D morphable model is used to generate 3D face models from three input images from each person in the training database. The 3D models are rendered under varying pose and illumination conditions to build a large set of synthetic images. These images are then used to train a component-based face recognition system. The resulting system achieved 90% accuracy on a database of 1200 real images of six people and significantly outperformed a comparable global face recognition system. The results show the potential of the combination of morphable models and component-based recognition towards pose and illumination invariant face recognition based on only three training images of each subject.


Face Recognition Training Image Synthetic Image Linear Support Vector Machine Face Recognition System 
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 2003

Authors and Affiliations

  • Jennifer Huang
    • 1
  • Bernd Heisele
    • 1
    • 2
  • Volker Blanz
    • 3
  1. 1.Center for Biological and Computational Learning, M.I.T.CambridgeUSA
  2. 2.Honda Research Institute USBostonUSA
  3. 3.Computer Graphics GroupMax-Planck-InstitutSaarbrückenGermany

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