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


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.


Face recognition Kernel function Nearest-farthest subspace classifier 



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.


  1. 1.
    Abdi H, Williams LJ (2010) Principal Component Analysis. Wiley Interdisciplinary Reviews Computational Statistics 2(4):433–459CrossRefGoogle Scholar
  2. 2.
    Abeni P, Baltatu M, Dalessandro R (2006) User Authentication based on Face Recognition with Support Vector Machines. The Canadian Conference on Computer and Robot Vision:42–42Google Scholar
  3. 3.
    Argyri AA, Panagou EZ, Tarantilis PA et al (2010) Rapid Qualitative and Quantitative Detection of Beef Fillets Spoilage Based on Fourier Transform Infrared Spectroscopy Data and Artificial Neural Networks. Sensors Actuators B Chem 145(1):146–154CrossRefGoogle Scholar
  4. 4.
    Bartlett MS, Lades MH, Sejnowski TJ (1998) Independent Component Representations for Face Recognition. Proceedings of SPIE-The. Int Soc Opt Eng 3299:528–539Google Scholar
  5. 5.
    Basri R, Jacobs D (2003) Lambertian Reflectance and Linear Subspaces. IEEE Transactions on Pattern Analysis & Machine Intelligence 25(2):218–233CrossRefGoogle Scholar
  6. 6.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis & Machine Intelligence 19(7):711–720CrossRefGoogle Scholar
  7. 7.
    Chai X, Shan S, Chen X et al (2007) Locally Linear Regression for Pose-Invariant Face Recognition. IEEE Trans Image Process 16(7):1716–1725MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chien Y (1973) Pattern classification and scene analysis. Wiley 19(4):462–463Google Scholar
  9. 9.
    Comon BP (2014) Independent Component Analysis: A New Concept? Signal Process 36:11–20Google Scholar
  10. 10.
    Feng Q, Yuan C, Huang J et al (2015) Center-based Weighted Kernel Linear Regression for Image Classification. IEEE International Conference on Image Processing:3630–3634Google Scholar
  11. 11.
    Gao QB, Wang ZZ (2007) Center-based Nearest Neighbor Classifier. Pattern Recogn 40(1):346–349CrossRefGoogle Scholar
  12. 12.
    Grauman K, Darrell T (2005) The pyramid match kernel: Discriminative classification with sets of image features. Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on. IEEE 2:1458–1465Google Scholar
  13. 13.
    Huang SM, Yang JF (2013) Linear Discriminant Regression Classification for Face Recognition. IEEE Signal Processing Letters 20(1):91–94CrossRefGoogle Scholar
  14. 14.
    Lades M, Vorbruggen JC, Buhmann J et al (1993) Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans Comput 42(3):300–311CrossRefGoogle Scholar
  15. 15.
    Leng L, Zhang J, Chen G, et al (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. International Conference on Computational Science and Its Applications. Springer, Berlin, Heidelberg, pp. 458–470CrossRefGoogle Scholar
  16. 16.
    Leng L, Zhang J, Khan MK et al (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. International Journal of Physical Sciences 5(17):2543–2554Google Scholar
  17. 17.
    Leng L, Zhang J, Xu J, et al (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. Information and Communication Technology Convergence (ICTC), 2010 International Conference on. IEEE, pp. 467–471Google Scholar
  18. 18.
    Li H, Wang S, Qi F (2004) Automatic Face Recognition by Support Vector Machines. 3322:716–725Google Scholar
  19. 19.
    Lu Y, Fang X, Xie B (2014) Kernel Linear Regression for Face Recognition. Neural Comput & Applic 24(7–8):1843–1849CrossRefGoogle Scholar
  20. 20.
    Mas JF, Flores JJ (2008) The Application of Artificial Neural Networks to the Analysis of Remotely Sensed Data. Int J Remote Sens 29(3):617–663CrossRefGoogle Scholar
  21. 21.
    Mi JX, Huang DS, Wang B et al (2013) The Nearest-Farthest Subspace Classification for Face Recognition. Neurocomputing 113(7):241–250CrossRefGoogle Scholar
  22. 22.
    Naseem I, Togneri R, Bennamoun M (2010) Linear Regression for Face Recognition. IEEE Trans Softw Eng 32(11):2106–2112Google Scholar
  23. 23.
    Nastar C, Ayach N, Nastar C et al (1996) Frequency-based Nonrigid Motion Analysis. IEEE Trans Pattern Anal Mach Intell (11):18Google Scholar
  24. 24.
    Pan JS, Feng Q, Yan L et al (2015) Neighborhood Feature Line Segment for Image Classification. IEEE Transactions on Circuits & Systems for Video Technology 25(3):387–398CrossRefGoogle Scholar
  25. 25.
    Peng Y, Ke J, Liu S et al (2018) An improvement to linear regression classification for face recognition. Int J Mach Learn Cybern:1–15Google Scholar
  26. 26.
    Tsai PW, Khan MK, Pan JS et al (2014) Interactive artificial bee colony supported passive continuous authentication system. IEEE Syst J 8(2):395–405CrossRefGoogle Scholar
  27. 27.
    Turk M, Pentland A (1991) Eigenfaces for Recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  28. 28.
    Wright J, Ma Y, Mairal J et al (2010) Sparse Representation for Computer Vision and Pattern Recognition. Proc IEEE 98(6):1031–1044CrossRefGoogle Scholar
  29. 29.
    Xu Y, Zhang D, Yang J et al (2011) A Two-Phase Test Sample Sparse Representation Method for Use with Face Recognition. IEEE Transactions on Circuits & Systems for Video Technology 21(9):1255–1262MathSciNetCrossRefGoogle Scholar

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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