Advertisement

Research on Face Recognition Method Based on Combination of SVM and LDA-PCA

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

Abstract

In the face recognition, the nonlinear factors, such as the light, expression and gesture, has great changes, the recognition effect of the PCA algorithm has been seriously influenced. This paper proposes an LDA-PCA algorithm, that merged the idea of LDA (Linear Discriminant Analysis) into PCA algorithm, to obtain the eigen-face subspace and use of the LDA's idea to discriminate and analyze, and then select the feature face vector, mainly reflecting the category difference, to form a new subspace. Then taking use of SVM classifier on the new subspace. The simulation results, on the improved face database, show that the LDA-PCA algorithm can effectively improve the robustness of nonlinear factor and the face recognition rate.

Keywords

Principal component analysis (PCA) Linear discriminant analysis (LDA) Support vector machine (SVM) Face recognition 

Notes

Acknowledgment

This research was supported by the Tianjin natural science Fund (13JCYBJC15800).

References

  1. 1.
    Pantic M, Rothkrantz LJM (2000) Automatic analysis of facial expressions: the state of the art. IEEE Trans Pattern Anal Mach Intell 22(12):1424–1445CrossRefGoogle Scholar
  2. 2.
    Peng H, Zhang C, Rong G et al (1997) Research of automated face recognition based on K-L transform. J Tsinghua Univ (Sci Tech) 3:67–70Google Scholar
  3. 3.
    Sun D, Li L, Wu L (2001) Face recognition feature space optimization method. Signal Process 17(6):510–514Google Scholar
  4. 4.
    Yang J, Zhang D, Frangi A et al (2004) Two-dimensional PCA: a new approach to appearance based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137CrossRefGoogle Scholar
  5. 5.
    Chen F, Chen X, Zhang S et al (2006) A human face recognition method based on modular 2DPCA. J Image Graph 11(4):580–585Google Scholar
  6. 6.
    Zhang L (2010) Face recognition method using improved modular 2DPCA. Comput Eng Appl 46(13):147–150Google Scholar
  7. 7.
    Sun Z, Wang F, Wu X (2012) Face recognition based on modular Kernel principal component analysis. Comput Digit Eng 40(8):119–121Google Scholar
  8. 8.
    Bian Z, Zhang X (2007) Pattern recognition. Tsinghua University, Beijing, pp 87–91, 222–226Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ruian Liu
    • 1
  • Junsheng Zhang
    • 1
  • Lei Wang
    • 1
  • Mimi Zhang
    • 1
  1. 1.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina

Personalised recommendations