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

McCool, Christopher S., Sridharan, Sridha, Chandran, Vinod, & Cook, Jamie A. (2006) Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition. In IEEE International Conference on Video and Signal Based Surveillance, 2006. AVSS '06, 22-24 November 2006, Sydney, NSW.

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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.

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3 citations in Scopus
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ID Code: 9353
Item Type: Conference Paper
Refereed: No
DOI: 10.1109/AVSS.2006.50
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 05 Sep 2007 00:00
Last Modified: 29 Feb 2012 13:22

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