Incremental perspective motion model for rigid and non-rigid motion separation

Tzung Heng Lai, Te Hsun Wang, Jenn Jier James Lien

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Motion extraction is an essential work in facial expression analysis because facial expression usually experiences rigid head rotation and non-rigid facial expression simultaneously. We developed a system to separate non-rigid motion from large rigid motion over an image sequence based on the incremental perspective motion model. Since the parameters of this motion model are able to not only represnt the global rigid motion but also localize die non-rigid motion, thus this motion model overcomes the limitations of existing memods, the affine model and the 8-parameter perspective projection model, in large head rotation angles. In addition, since the gradient descent approach is susceptible to local minimum during the motion parameter estimation process, a multi-resolution approach is applied to optimize initial values of parameters at the coarse level. Finally, the experimental result shows that our model has promising performance of separating non-rigid motion from rigid motion.

Original languageEnglish
Title of host publicationAdvances in Image and Video Technology - Second Pacific Rim Symposium, PSIVT 2007, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540771289
Publication statusPublished - 2007
Event2nd Pacific Rim Symposium on Image and Video Technology, PSIVT 2007 - Santiago, Chile
Duration: 2007 Dec 172007 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4872 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd Pacific Rim Symposium on Image and Video Technology, PSIVT 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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