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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 32007–32021 | Cite as

Kernel nearest-farthest subspace classifier for face recognition

  • Linlin TangEmail author
  • Zuohua Li
  • Jingyong Su
  • Huifen Lu
  • Zhangyan Li
  • Zhen Pang
  • Yong Zhang
Article
  • 41 Downloads

Abstract

In this paper, a novel classifier named Kernel Nearest-Farthest Subspace (KNFS) classifier is proposed for face recognition. Inspired by the kernel-based classifier and the Nearest-Farthest Subspace (NFS) classifier, KNFS can make the sample points to be linear separable by utilizing the kernel function to map linear inseparable sample points in low-dimensional space to high-dimensional kernel space. And it can improve the recognition accuracy of crossed sample points between classes. The algorithm provides the highest reported recognition accuracy on AR and AT&T database. The results are comparable with many other state-of-art face recognition algorithms.

Keywords

Face recognition Kernel function Nearest-farthest subspace classifier 

Notes

Acknowledgements

This work was supported by Shenzhen Science and Technology Plan Fundamental Research Funding JCYJ20180306171938767 and Shenzhen Foundational Research Funding JCYJ20180507183527919. And it was also partly supported by the Shenzhen Technology Innovation with grant number JCYJ20170811160003571 and JCYJ20170302145623566.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Linlin Tang
    • 1
    Email author
  • Zuohua Li
    • 1
  • Jingyong Su
    • 2
  • Huifen Lu
    • 1
  • Zhangyan Li
    • 1
  • Zhen Pang
    • 1
  • Yong Zhang
    • 3
  1. 1.Harbin Institute of TechnologyShenzhenChina
  2. 2.Texas Tech UniversityLubbockUSA
  3. 3.Shenzhen UniversityShenzhenChina

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