Facial feature extraction and image warping using PCA based statistic model

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

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 proceeding › Conference contribution

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 language	English
Title of host publication	IEEE International Conference on Image Processing
Pages	689-692
Number of pages	4
Volume	2
State	Published - Jan 1 2001
Event	IEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece Duration: Oct 7 2001 → Oct 10 2001

Other

Other	IEEE International Conference on Image Processing (ICIP)
Country/Territory	Greece
City	Thessaloniki
Period	10/7/01 → 10/10/01

ASJC Scopus subject areas

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

Cite this

@inproceedings{ca157a135fe747f0b565747f2401b728,

title = "Facial feature extraction and image warping using PCA based statistic model",

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.",

author = "Z. Xue and Li, {S. Z.} and Teoh, {E. K.}",

year = "2001",

month = jan,

day = "1",

language = "English",

volume = "2",

pages = "689--692",

booktitle = "IEEE International Conference on Image Processing",

note = "IEEE International Conference on Image Processing (ICIP) ; Conference date: 07-10-2001 Through 10-10-2001",

}

TY - GEN

T1 - Facial feature extraction and image warping using PCA based statistic model

AU - Xue, Z.

AU - Li, S. Z.

AU - Teoh, E. K.

PY - 2001/1/1

Y1 - 2001/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0035168482&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035168482&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0035168482

VL - 2

SP - 689

EP - 692

BT - IEEE International Conference on Image Processing

T2 - IEEE International Conference on Image Processing (ICIP)

Y2 - 7 October 2001 through 10 October 2001

ER -

Facial feature extraction and image warping using PCA based statistic model

Abstract

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ASJC Scopus subject areas

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