Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition

Sydney, NSW, Australia November 22-November 24

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http://doi.ieeecomputersociety.org/10.1109/AVSS.2006.50

2006 IEEE International Conference on ...

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	ASCII Text	x
	Chris McCool, Jamie Cook, Vinod Chandran, Sridha Sridharan, "Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition," Advanced Video and Signal Based Surveillance, IEEE Conference on, pp. 57, 2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06), 2006.

	BibTex	x
	@article{ 10.1109/AVSS.2006.50, author = {Chris McCool and Jamie Cook and Vinod Chandran and Sridha Sridharan}, title = {Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition}, journal ={Advanced Video and Signal Based Surveillance, IEEE Conference on}, volume = {0}, year = {2006}, isbn = {0-7695-2688-8}, pages = {57}, doi = {http://doi.ieeecomputersociety.org/10.1109/AVSS.2006.50}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Advanced Video and Signal Based Surveillance, IEEE Conference on TI - Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition SN - 0-7695-2688-8 SP EP A1 - Chris McCool, A1 - Jamie Cook, A1 - Vinod Chandran, A1 - Sridha Sridharan, PY - 2006 KW - null VL - 0 JA - Advanced Video and Signal Based Surveillance, IEEE Conference on ER -

Chris McCool, Queensland University of Technology, Australia

Jamie Cook, Queensland University of Technology, Australia

Vinod Chandran, Queensland University of Technology, Australia

Sridha Sridharan, Queensland University of Technology, Australia

This paper examines the the effectiveness of feature modelling to conduct 2D and 3D face recognition. In particular, PCA difference vectors are modelled using Gaussian Mixture Models (GMMs) which describe Intra-Personal (IP) and Extra-Personal (EP) variations. Two classifiers, an IP and IPEP classifier, are formed using these GMMs and their performance is compared to that of the Mahalanobis cosine metric (MahCosine). The best results for the 2D and 3D face modalities are obtained with the IP and IPEP classifiers respectively. The multi-modal fusion of these two systems provided consistent performance improvement across the FRGC database v2.0.

Citation:

Chris McCool, Jamie Cook, Vinod Chandran, Sridha Sridharan, "Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition," avss, pp.57, 2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06), 2006

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