Facial modeling from an uncalibrated face image using a coarse-to-fine genetic algorithm

Shinn-Ying Ho*, Hui Ling Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

This paper presents a genetic algorithm-based optimization approach for facial modeling from an uncalibrated face image using a flexible generic parameterized facial model (FGPFM). The FGPFM can be easily modified using the facial features as parameters of FGPFM to construct an accurate specific 3D facial model from only a photograph of an individual with a yawed face based on the projection transformation. The facial modeling problem is formulated as a parameter optimization problem and the objective function is also given. Moreover, a coarsc-to-fine approach based on our intelligent genetic algorithm which can efficiently solve the large parameter optimization problems is used to accelerate the search for an optimal solution. Furthermore, sensitivity analysis and experimental results with texture mapping demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1015-1031
Number of pages17
JournalPattern Recognition
Volume34
Issue number5
DOIs
StatePublished - 1 May 2001

Keywords

  • Computer vision
  • Facial modeling
  • Generic facial model
  • Genetic algorithm
  • Optimization
  • Pose determination

Fingerprint Dive into the research topics of 'Facial modeling from an uncalibrated face image using a coarse-to-fine genetic algorithm'. Together they form a unique fingerprint.

Cite this