3D facial tracking from corrupted movie sequences

Siome Goldenstein, Christian Vogler, Dimitris Metaxas

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


In this paper we perform 3D face tracking on corrupted video sequences. We use a deformable model, combined with a predictive filter, to recover both the rigid transformations and the values of the parameters that describe the evolution of the facial expressions over time. To be robust, predictive filters need a good observation of the system's state. We describe a new method to measure, at each moment in time, the correct distribution of an observation of the parameters of a high-dimensional deformable model. This method is based on bounding the confidence regions of the 2D image displacements with affine forms, and propagating them into parameter space. Using Lindeberg's theorem, we measure a good Gaussian approximation of the parameters in a manner that avoids many of the traditional assumptions about the observations' distributions. We demonstrate in experiments on sequences with compression artifacts, and poor-quality video sequences of Lauren Bacall and Humphrey Bogart from the 1950s, that, without any learning involved, our method is sufficiently robust to extract information from degraded image sequences. In addition, we provide ground truth validation.

Original languageEnglish (US)
Pages (from-to)I880-I885
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2004
EventProceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004 - Washington, DC, United States
Duration: Jun 27 2004Jul 2 2004

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


  • 3D face tracking
  • Deformable models
  • Kalman filter
  • Parametric estimation
  • Particle Filter


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