Rapid and brief communication: 2D clustering based discriminant analysis for 3D head model classification

Authors:
Bo Ma;Hau-San Wong
Affiliations:
Department of Computer Science, City University of Hong Kong, Hong Kong;Department of Computer Science, City University of Hong Kong, Hong Kong
Venue:
Pattern Recognition
Year:
2006

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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.