article

Symmetrical null space LDA for face and ear recognition

Authors:

Jia YundeAuthors Info & Claims

Neurocomputing, Volume 70, Issue 4-6

Pages 842 - 848

https://doi.org/10.1016/j.neucom.2006.10.016

Published: 01 January 2007 Publication History

Abstract

Many natural objects such as face and ear manifest symmetry. The mirror images of symmetrical objects also encode significant discriminative information, which is of benefit to recognition performance. In this paper, a novel symmetrical null space method with the even-odd decomposition principle is proposed for face and ear recognition. By introducing mirror images, the two orthogonal even/odd eigenspaces are constructed. Then the discriminative features are, respectively, extracted from the two eigenspaces under the most suitable situation of the null space. Finally, all the features are combined for classification. Experimental results on both face database and ear database demonstrate the performance of the proposed method.

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Cited By

Pattnaik IDev AMohapatra A(2023)A face recognition taxonomy and review framework towards dimensionality, modality and feature qualityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107056126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.107056
Yuan LLiu WLi Y(2016)Non-negative dictionary based sparse representation classification for ear recognition with occlusionNeurocomputing10.1016/j.neucom.2015.06.074171:C(540-550)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.06.074
Abaza ARoss AHebert CHarrison MNixon M(2013)A survey on ear biometricsACM Computing Surveys10.1145/2431211.243122145:2(1-35)Online publication date: 12-Mar-2013
https://dl.acm.org/doi/10.1145/2431211.2431221
Show More Cited By

Index Terms

Symmetrical null space LDA for face and ear recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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Published In

cover image Neurocomputing

Neurocomputing Volume 70, Issue 4-6

January, 2007

494 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2006.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2007

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Cited By

Pattnaik IDev AMohapatra A(2023)A face recognition taxonomy and review framework towards dimensionality, modality and feature qualityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107056126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.107056
Yuan LLiu WLi Y(2016)Non-negative dictionary based sparse representation classification for ear recognition with occlusionNeurocomputing10.1016/j.neucom.2015.06.074171:C(540-550)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.06.074
Abaza ARoss AHebert CHarrison MNixon M(2013)A survey on ear biometricsACM Computing Surveys10.1145/2431211.243122145:2(1-35)Online publication date: 12-Mar-2013
https://dl.acm.org/doi/10.1145/2431211.2431221
Islam SBennamoun MOwens RDavies R(2012)A review of recent advances in 3D ear- and expression-invariant face biometricsACM Computing Surveys10.1145/2187671.218767644:3(1-34)Online publication date: 14-Jun-2012
https://dl.acm.org/doi/10.1145/2187671.2187676
Huang HLiu JFeng HHe T(2011)Ear recognition based on uncorrelated local Fisher discriminant analysisNeurocomputing10.1016/j.neucom.2011.04.02274:17(3103-3113)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1016/j.neucom.2011.04.022

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