Bayesian shape model for facial feature extraction and recognition

Zhong Xue, Stan Z. Li, Eam Khwang Teoh

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

A facial feature extraction algorithm using the Bayesian shape model (BSM) is proposed in this paper. A full-face model consisting of the contour points and the control points is designed to describe the face patch, using which the warping/normalization of the extracted face patch can be performed efficiently. First, the BSM is utilized to match and extract the contour points of a face. In BSM, the prototype of the face contour can be adjusted adaptively according to its prior distribution. Moreover, an affine invariant internal energy term is introduced to describe the local shape deformations between the prototype contour in the shape domain and the deformable contour in the image domain. Thus, both global and local shape deformations can be tolerated. Then, the control points are estimated from the matching result of the contour points based on the statistics of the full-face model. Finally, the face patch is extracted and normalized using the piece-wise affine triangle warping algorithm. Experimental results based on real facial feature extraction demonstrate that the proposed BSM facial feature extraction algorithm is more accurate and effective as compared to that of the active shape model (ASM).

Original languageEnglish (US)
Pages (from-to)2819-2833
Number of pages15
JournalPattern Recognition
Volume36
Issue number12
DOIs
StatePublished - Jan 1 2003

Keywords

  • Active shape model
  • Bayesian shape model
  • Face recognition
  • Facial feature extraction
  • Principle component analysis

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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