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)


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.


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



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


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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

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