Robust tracking of facial feature points with 3D Active Shape Models

Moritz Kaiser, Dejan Arsić, Shamik Sural, Gerhard Rigoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Exact 3D tracking of facial feature points is appealing for many applications in human-machine interaction. In this work a 3D Active Shape Model (ASM) that can be shifted, scaled, and rotated is used to track the points. The efficient Gauss-Newton method is applied to estimate the 3D ASM, rotation, translation, and scale parameters. If the head turns to one side, some points might be occluded but they are still considered for the estimation of the parameters. A robust error norm that reduces (or ideally cancels) the influence of occluded points is applied. With some algebraic transformations the computational cost per frame can be further reduced. The proposed algorithm is evaluated on the basis of the Airplane Behavior Corpus.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages3829-3832
Number of pages4
DOIs
StatePublished - 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period26/09/1029/09/10

Keywords

  • Face recognition
  • Minimization methods
  • Robustness
  • Tracking

Fingerprint

Dive into the research topics of 'Robust tracking of facial feature points with 3D Active Shape Models'. Together they form a unique fingerprint.

Cite this