Facial feature extraction and image warping using PCA based statistic model

Z. Xue, S. Z. Li, E. K. Teoh

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

11 Scopus citations

Abstract

A new algorithm is proposed to extract the facial features and estimate the control points for facial image warping using the Principle Component Analysis (PCA) based statistic face model. In this algorithm, first a full-face model consisting the contour points and the control points is built. Based on a number of manually marked training samples, the prior distribution of the full-face model can be obtained by using the PCA. Given an input face image, first the contour points are obtained by using the recently developed Bayesian Shape Model (BSM), and then the control points are estimated from the contour points. Finally, the extracted face path is normalized using the piece-wise affine triangle warping algorithm. Experimental results illustrate the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Pages689-692
Number of pages4
Volume2
StatePublished - Jan 1 2001
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001

Other

OtherIEEE International Conference on Image Processing (ICIP)
CountryGreece
CityThessaloniki
Period10/7/0110/10/01

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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