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Tensor-based Multivariate Statistical Discriminant Methods for Face Applications

This paper describes the use of tensor-based multivariate statistical discriminant methods in three-dimensional face applications for synthesis and modelling of face shapes and for recognition. The methods could recognise faces and facial expressions, synthesize new face shapes and generate facial e...

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Main Authors: Jacey-Lynn, Minoi, Thomaz, Carlos E., Gillies, Duncan Fyfe
Format: E-Article
Language:English
Published: IEEE 2012
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/16600/1/Tensor-based%20Multivariate%20Statistical%20Discriminant%28abstract%29.pdf
http://ir.unimas.my/id/eprint/16600/
http://ieeexplore.ieee.org/document/6396626/
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spelling my.unimas.ir.166002017-06-12T04:16:43Z http://ir.unimas.my/id/eprint/16600/ Tensor-based Multivariate Statistical Discriminant Methods for Face Applications Jacey-Lynn, Minoi Thomaz, Carlos E. Gillies, Duncan Fyfe T Technology (General) This paper describes the use of tensor-based multivariate statistical discriminant methods in three-dimensional face applications for synthesis and modelling of face shapes and for recognition. The methods could recognise faces and facial expressions, synthesize new face shapes and generate facial expressions based on the the most discriminant vectors calculated in the training sets that contain classes of face shapes and facial expressions. The strength of the introduced methods is that varying degrees of face shapes can be generated given that only a small number of 3D face shapes are available in the dataset. This framework also has the ability to characterise face variations across subjects and facial expressions. Recognition experiment was conducted using 3D face database created by the State University of New York (SUNY), Binghamton. The results have shown higher recognition rates for face and facial expression compared to the more popular eigenface techniques. The outcome of the synthesis of face shapes and facial expressions will also be presented here. IEEE 2012 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/16600/1/Tensor-based%20Multivariate%20Statistical%20Discriminant%28abstract%29.pdf Jacey-Lynn, Minoi and Thomaz, Carlos E. and Gillies, Duncan Fyfe (2012) Tensor-based Multivariate Statistical Discriminant Methods for Face Applications. International Conference on Statistics in Science, Business, and Engineering (ICSSBE), 2012. ISSN ISBN: 978-1-4673-1582-1 http://ieeexplore.ieee.org/document/6396626/ 10.1109/ICSSBE.2012.6396626
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Jacey-Lynn, Minoi
Thomaz, Carlos E.
Gillies, Duncan Fyfe
Tensor-based Multivariate Statistical Discriminant Methods for Face Applications
description This paper describes the use of tensor-based multivariate statistical discriminant methods in three-dimensional face applications for synthesis and modelling of face shapes and for recognition. The methods could recognise faces and facial expressions, synthesize new face shapes and generate facial expressions based on the the most discriminant vectors calculated in the training sets that contain classes of face shapes and facial expressions. The strength of the introduced methods is that varying degrees of face shapes can be generated given that only a small number of 3D face shapes are available in the dataset. This framework also has the ability to characterise face variations across subjects and facial expressions. Recognition experiment was conducted using 3D face database created by the State University of New York (SUNY), Binghamton. The results have shown higher recognition rates for face and facial expression compared to the more popular eigenface techniques. The outcome of the synthesis of face shapes and facial expressions will also be presented here.
format E-Article
author Jacey-Lynn, Minoi
Thomaz, Carlos E.
Gillies, Duncan Fyfe
author_facet Jacey-Lynn, Minoi
Thomaz, Carlos E.
Gillies, Duncan Fyfe
author_sort Jacey-Lynn, Minoi
title Tensor-based Multivariate Statistical Discriminant Methods for Face Applications
title_short Tensor-based Multivariate Statistical Discriminant Methods for Face Applications
title_full Tensor-based Multivariate Statistical Discriminant Methods for Face Applications
title_fullStr Tensor-based Multivariate Statistical Discriminant Methods for Face Applications
title_full_unstemmed Tensor-based Multivariate Statistical Discriminant Methods for Face Applications
title_sort tensor-based multivariate statistical discriminant methods for face applications
publisher IEEE
publishDate 2012
url http://ir.unimas.my/id/eprint/16600/1/Tensor-based%20Multivariate%20Statistical%20Discriminant%28abstract%29.pdf
http://ir.unimas.my/id/eprint/16600/
http://ieeexplore.ieee.org/document/6396626/
_version_ 1644512411031437312
score 13.211869

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