2D clustering based discriminant analysis for 3D head model classification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)491-494
Journal / PublicationPattern Recognition
Volume39
Issue number3
Publication statusPublished - Mar 2006

Abstract

This paper introduces a novel framework for 3D head model recognition based on the recently proposed 2D subspace analysis method. Two main contributions have been made. First, a 2D version of clustering-based discriminant analysis (CDA) is proposed, which combines the capability to model the multiple cluster structure embedded within a single class with the computational advantage that is characteristic of 2D subspace analysis methods. Second, we extend the applications of 2D subspace methods to the field of 3D head model classification by characterizing these models with 2D feature sets. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Research Area(s)

  • 2D clustering-based discriminant analysis, 2D Fisher discriminant analysis, 2D subspace analysis, 3D head model classification, Extended Gaussian image