Comparison of PCA, LDA and Gabor Features for Face Recognition Using Fuzzy Neural Network

  • Dhanya S. PankajEmail author
  • M. Wilscy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


A face recognition system identifies or verifies face images from a stored database of faces when a still image or a video is given as input. The recognition accuracy depends on the features used to represent the face images. In this paper a comparison of three popular features – PCA, LDA and Gabor features - used in literature to represent face images is given. The classifier used is a Fuzzy Neural Network classifier. The comparison was performed using AT&T, Yale and Indian databases. From the experimental results, the LDA features provide better Recognition Rates in the case of face images with less pose variations. Where more pose variations are involved, the Gabor features performed better than LDA features. For recognition tasks where recognition of trained individuals and rejection of untrained individuals are considered, the LDA features provide better results in terms of very low False Acceptance Rates and False Rejection Rates.


Face Recognition PCA LDA Gabor Fuzzy Neural IAFC 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Su, Y., Shan, S., Chen, X., Ga, W.: Hierarchical Ensemble of Global and Local Classifiers for Face Recognition. IEEE Transactions on Image Processing 18 (2009)Google Scholar
  2. 2.
    Lin, S.-H.: An Introduction to Face Recognition Technology. Informing Science Special Issue on Multimedia Informing Technologies – Part 2 3(1) (2000)Google Scholar
  3. 3.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399-458 (2003)Google Scholar
  4. 4.
    Lu, J., Yuan, X., Yahagi, T.: A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs. IEEE Transactions on Neural Networks 18(1) (2007)Google Scholar
  5. 5.
    Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of Principal Component For Face Recognition. IEEE Trans. Pattern Anal. Machine Intel. 8 (1997)Google Scholar
  6. 6.
    Du., S., Ward, R.K.: Improved Face Representation by Nonuniform Multilevel Selection of Gabor Convolution Features. IEEE Transactions on Systems, Man, and Cybernetics—Part b: Cybernetics 39(6) (2009)Google Scholar
  7. 7.
    Moghaddam, B.: Principal manifolds And Probabilistic Subspaces For Visual Recognition. IEEE Trans. Pattern Anal. Machine Intel. 24(6), 780–788 (2002)CrossRefGoogle Scholar
  8. 8.
    Othman, H., Aboulnasr, T.: A Separable Low Complexity 2D HMM with Application to Face Recognition. IEEE Trans. Pattern. Anal. Machine Intel. 25(10), 1229–1238 (2003)CrossRefGoogle Scholar
  9. 9.
    Er, M., Wu, S., Lu, J., Toh, L.H.: Face recognition with Radial Basis Function (RBF) Neural Networks. IEEE Trans. Neural Networks 13(3), 697–710 (1999)Google Scholar
  10. 10.
    Lee, K., Chung, Y., Byun, H.: SVM Based Face Verification With Feature Set of Small Size. Electronic Letters 38(15), 787–789 (2002)CrossRefGoogle Scholar
  11. 11.
    Kim, Y.S., Mitra, S.: An Adaptive Integrated Fuzzy Clustering Model for Pattern Recognition. Journal Fuzzy Sets and Systems (65), 297–310 (1994)Google Scholar
  12. 12.
    Delac, K., Grgic, M., Grgic, S.: Independent Comparative Study of PCA, ICA, And LDA on the Feret Data Set. International Journal of Imaging Systems and Technology 15(5), 252–260 (2005)CrossRefGoogle Scholar
  13. 13.
    Cho, H., Moon, S.: Comparison of PCA and LDA Based Face Recognition Algorithms Under Illumination Variations. In: ICCAS-SICE, pp. 4025–4030 (2009)Google Scholar
  14. 14.
    Luo, B., Hao, Y.-J., Zhang, W.-H., Liu, Z.-S.: Comparison of PCA and ICA in Face Recognition. In: International Conference on Apperceiving Computing and Intelligence Analysis, pp. 241–243 (2008)Google Scholar
  15. 15.
    Pankaj, D.S., Wilscy, M.: Face Recognition Using Fuzzy Neural Network Classifier. Advances in Parallel Distributed Computing, 53–62 (2011)Google Scholar
  16. 16.
  17. 17.
  18. 18.
    Jain, V., Mukherjee, A.: The Indian Face Database,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Rajagiri School of Engineering and TechnologyErnakulamIndia
  2. 2.Department of Computer ScienceUniversity of KeralaKariyavattomIndia

Personalised recommendations