3D Morphable Model Parameter Estimation

  • Nathan Faggian
  • Andrew P. Paplinski
  • Jamie Sherrah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


Estimating the structure of the human face is a long studied and difficult task. In this paper we present a new method for estimating facial structure from only a minimal number of salient feature points across a video sequence. The presented method uses both an Extended Kalman Filter (EKF) and a Kalman Filter (KF) to regress 3D Morphable Model (3DMM) shape parameters and solve 3D pose using a simplified camera model. A linear method for initializing the recursive pose filter is provided. The convergence properties of the method are then evaluated using synthetic data. Finally, using the same synthetic data the method is demonstrated for both single image shape recovery and shape recovery across a sequence.


Ground Truth Kalman Filter Extend Kalman Filter Active Appearance Model Exterior Orientation 
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 2006

Authors and Affiliations

  • Nathan Faggian
    • 1
  • Andrew P. Paplinski
    • 1
  • Jamie Sherrah
    • 2
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia
  2. 2.Clarity Visual IntelligenceAustralia

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