Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition
(2006) Feature Modelling of PCA Difference Vectors for 2D and 3D Face Recognition. In Proceedings IEEE International Conference on Video and Signal Based Surveillance, 2006. AVSS '06, Sydney, NSW.
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Abstract
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.
| Item Type: | Conference Paper |
|---|---|
| RM Number: | 2007004506 |
| Status: | Published |
| Subjects: | Subjects UNSPECIFIED |
| ID Code: | 9353 |
| Deposited By: | Bozzetto, Adam |
| Deposited On: | 05 September 2007 |
| Alternative Locations: | http://dx.doi.org/10.1109/AVSS.2006.50 |
| 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. |