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2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06)
Human Face Reconstruction Using Bayesian Deformable Models
Sydney, NSW, Australia
November 22-November 24
ISBN: 0-7695-2688-8
ASCII Text
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George Mamic, Clinton Fookes, Sridha Sridharan, "Human Face Reconstruction Using Bayesian Deformable Models," Advanced Video and Signal Based Surveillance, IEEE Conference on, pp. 59, 2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06), 2006.
 
 
BibTex
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@article{ 10.1109/AVSS.2006.60,
author = {George Mamic and Clinton Fookes and Sridha Sridharan},
title = {Human Face Reconstruction Using Bayesian Deformable Models},
journal ={Advanced Video and Signal Based Surveillance, IEEE Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2688-8},
pages = {59},
doi = {http://doi.ieeecomputersociety.org/10.1109/AVSS.2006.60},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Advanced Video and Signal Based Surveillance, IEEE Conference on
TI - Human Face Reconstruction Using Bayesian Deformable Models
SN - 0-7695-2688-8
SP
EP
A1 - George Mamic,
A1 - Clinton Fookes,
A1 - Sridha Sridharan,
PY - 2006
KW - null
VL - 0
JA - Advanced Video and Signal Based Surveillance, IEEE Conference on
ER -
 
George Mamic, Queensland University of Technology, Australia
Clinton Fookes, Queensland University of Technology, Australia
Sridha Sridharan, Queensland University of Technology, Australia
This paper presents a Bayesian framework for 3D facial reconstruction. The framework iteratively deforms a generic face mesh to fit a set of range points representing a face. The generic mesh is generated from the extensive FRGC database of face images. The deformation process is conducted within a Bayesian framework and is driven by a Markov Chain Monte Carlo (MCMC) sampler which uses information from the likelihood and prior distributions of the generic face mesh. The paper presents results on the construction of a generic face model, the deformation framework and fitting results to both synthetic and real data. The results verify the effectiveness of the proposed technique, accurately deforming a generic face mesh to captured 3D data points of human faces.
Citation:
George Mamic, Clinton Fookes, Sridha Sridharan, "Human Face Reconstruction Using Bayesian Deformable Models," avss, pp.59, 2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06), 2006
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