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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)

Abstract

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.

Keywords

Face recognition PCA SVM Gaussian kernel function Cross validation 

References

  1. 1.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: International Conference on Computer Research and Development (2011)Google Scholar
  2. 2.
    Sharma, S.K., Lagunas, E., Chatzinotas, S., et al.: Application of compressive sensing in cognitive radio communications: a survey. J. IEEE Commun. Surv. Tutorials, 1–1 (2016)Google Scholar
  3. 3.
    Li, W., Peng, M., Wang, Q.: Fault identification in PCA method during sensor condition monitoring in a nuclear power plant. J. Ann. Nucl. Energy 121, 135–145 (2018)CrossRefGoogle Scholar
  4. 4.
    Li, X., Liu, F.C., Liu, X., et al.: Parameter identification and optimization for a class of fractional-order chaotic system with time delay. J. Int. J. Model. Ident. Control 29(2) (2018)Google Scholar
  5. 5.
    Salo, F., Nassif, A.B., Essex, A.: Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. J. Comput. Netw. 148, 164–175 (2019)CrossRefGoogle Scholar
  6. 6.
    Javed, A.: Face recognition based on principal component analysis. J. Int. Image Graph. Sig. Process. (IJIGSP) 5(2) (2013)CrossRefGoogle Scholar
  7. 7.
    Bottou, L., Cortes, C., Denker, J.S., et al.: Comparison of classifier methods: a case study in handwritten digit recognition. In: International Conference on Pattern Recognition. IEEE Computer Society (1994)Google Scholar
  8. 8.
    Salo, F., Nassif, A.B., Essex, A.: Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. J. Comput. Netw. (2018)Google Scholar
  9. 9.
    Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Netw. 12(6) (1999)CrossRefGoogle Scholar
  10. 10.
    Tang, Y., Guo, W., Gao, J.: Efficient model selection for support vector machine with Gaussian Kernel function. In: IEEE Symposium on Computational Intelligence & Data Mining. IEEE (2009)Google Scholar
  11. 11.
    Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based machines. J. Mach. Learn. Res. 2(2), 265–292 (2001)zbMATHGoogle Scholar
  12. 12.
    Keerthi, S.S., Gilbert, E.G.: Convergence of a generalized SMO algorithm for SVM classifier design. J. Mach. Learn. 46(1), 351–360 (2002)CrossRefGoogle Scholar
  13. 13.
    Hao, Z., Yu, S., Yang, X., et al.: Online LS-SVM learning for classification problems based on incremental chunk. Advances in Neural Networks—ISNN 2004. Springer, Berlin (2004)CrossRefGoogle Scholar
  14. 14.
    Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., et al.: Improvements to the SMO algorithm for SVM regression. J. IEEE Trans. Neural Netw. 11(5), 1188–1193 (2000)CrossRefGoogle Scholar
  15. 15.
    Duan, K., Keerthi, S.S., Poo, A.N.: Evaluation of simple performance measures for tuning SVM hyperparameters. J. Neurocomput. 51, 41–59 (2003)CrossRefGoogle Scholar
  16. 16.
    Wan, Y., Sheng, S., Huang, J., Li, X., Chen, X.: The grid search algorithm of tectonic stress tensor based on focal mechanism data and its application in the boundary zone of China, Vietnam and Laos. J. Earth Sci. 7(05), 777–785 (2006)CrossRefGoogle Scholar
  17. 17.
    Tian, W., Ye, X., Lei, W., et al.: Grid search optimized SVM method for dish-like underwater robot attitude prediction. In: Fifth International Joint Conference on Computational Sciences & Optimization (2012)Google Scholar
  18. 18.
    Kang, K., Qin, C., Lee, B., Lee, I.: Modified screening-based Kriging method with cross validation and application to engineering design. J. Appl. Math. Model. 70 (2019)MathSciNetCrossRefGoogle Scholar

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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