RESEARCH ARTICLE

3D face recognition based on principal axes registration and fusing features

  • Hongxia ZHANG ,
  • Yanning ZHANG ,
  • Zhe GUO ,
  • Zenggang LIN ,
  • Chao ZHANG
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  • Shaanxi Provincial Key Laboratory of Speech and Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

Received date: 23 Mar 2011

Accepted date: 29 Apr 2011

Published date: 05 Jun 2011

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

A 3D face recognition approach which uses principal axes registration (PAR) and three face representation features from the re-sampling depth image: Eigenfaces, Fisherfaces and Zernike moments is presented. The approach addresses the issue of 3D face registration instantly achieved by PAR. Because each facial feature has its own advantages, limitations and scope of use, different features will complement each other. Thus the fusing features can learn more expressive characterizations than a single feature. The support vector machine (SVM) is applied for classification. In this method, based on the complementarity between different features, weighted decision-level fusion makes the recognition system have certain fault tolerance. Experimental results show that the proposed approach achieves superior performance with the rank-1 recognition rate of 98.36% for GavabDB database.

Cite this article

Hongxia ZHANG , Yanning ZHANG , Zhe GUO , Zenggang LIN , Chao ZHANG . 3D face recognition based on principal axes registration and fusing features[J]. Frontiers of Electrical and Electronic Engineering, 2011 , 6(2) : 347 -352 . DOI: 10.1007/s11460-011-0155-x

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