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Published Articles >> Table of Contents >> Abstract
Second International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04)
pp. 502-509
Face Recognition from 3D Data using Iterative Closest Point Algorithm and Gaussian Mixture Models
Jamie Cook, Queensland University of Technology, Australia
Vinod Chandran, Queensland University of Technology, Australia
Sridha Sridharan, Queensland University of Technology, Australia
Clinton Fookes, Queensland University of Technology, Australia
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TDPVT.2004.1335279
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| Abstract |
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A new approach to face verification from 3D data is presented.
The method uses 3D registration techniques designed
to work with resolution levels typical of the irregular
point cloud representations provided by Structured Light
scanning. Preprocessing using a-priori information of the
human face and the Iterative Closest Point algorithm are
employed to establish correspondence between test and target
and to compensate for the non-rigid nature of the surfaces.
Statistical modelling in the form of Gaussian Mixture
Models is used to parameterise the distribution of errors in
facial surfaces after registration and is employed to differentiate
between intra- and extra-personal comparison of range
images. An Equal Error Rate of 2.67% was achieved on the
30 subject manual subset of the the 3d_rma database.
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 Additional Information
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Citation:
Jamie Cook, Vinod Chandran, Sridha Sridharan, Clinton Fookes,
"Face Recognition from 3D Data using Iterative Closest Point Algorithm and Gaussian Mixture Models,"
3dpvt,
pp. 502-509,
Second International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04),
2004
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