Research on Face Recognition Technology Based on PCA and SVM

  • Shu ZhangEmail author
  • Zi-Yue LiEmail author
  • Yu-Chao Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)


The PCA algorithm can simplify the high-dimensional problem into a low-dimensional problem. It is simple and fast, and the principal components are orthogonal to each other, which can eliminate the influence of the original data components. The face recognition technology based on PCA algorithm can remove noise caused by light, posture, and occlusion to some extent. The SVM method using kernel function can solve the nonlinear problem and has perfect classification effect. In this paper, combined with the PCA and SVM methods, dimension reduction and feature extraction are performed on the untrained images, and then the features are input into the SVM using the Gaussian kernel function for training. The performance of the SVM classifier is verified using 10-fold cross validation method. This method is suitable for scenes with high requirement for recognition speed, such as unmanned patrol car in industrial park.


Face recognition PCA SVM Gaussian kernel function Cross validation 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Engineering Laboratory for Integrated Command and Dispatch TechnologyBeijingChina
  2. 2.Navigation Research CenterNanjing University of Aeronautics and AstronauticsNanjingChina

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