Subspace Learning with Enriched Databases Using Symmetry

  • Konstantinos Papachristou
  • Anastasios Tefas
  • Ioannis Pitas
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 297)


Principal Component Analysis and Linear Discriminant Analysis are of the most known subspace learning techniques. In this paper, a way for training set enrichment is proposed in order to improve the performance of the subspace learning techniques by exploiting the a-priori knowledge that many types of data are symmetric. Experiments on artificial, facial expression recognition, face recognition and object categorization databases denote the robustness of the proposed approach.


Subspace Learning Data Enrichment Symmetry Principal Component Analysis Linear Discriminant Analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Professional (1990)Google Scholar
  2. 2.
    Jain, A., Duin, R., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  3. 3.
    Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer (2002)Google Scholar
  4. 4.
    Lee, T.-W.: Independent Component Analysis: Theory and Applications. Kluwer Academic Publishers (1998)Google Scholar
  5. 5.
    He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, vol. 16, pp. 153–160 (2003)Google Scholar
  6. 6.
    Lee, D., Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  7. 7.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(7), 179–188 (1936)CrossRefGoogle Scholar
  8. 8.
    Zafeiriou, S., Tefas, A., Buciu, I., Pitas, I.: Exploiting discriminant information in non-negative matrix factorization with application to frontal face verification. IEEE Transactions on Neural Networks 17(3), 683–695 (2006)CrossRefGoogle Scholar
  9. 9.
    Chen, X.-W., Huang, T.: Facial expression recognition: a clustering-based approach. Pattern Recognition Letters 24(9-10), 1295–1302 (2003)CrossRefMATHGoogle Scholar
  10. 10.
    Zhu, M., Martínez, A.: Subclass discriminant analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(8), 1274–1286 (2006)CrossRefGoogle Scholar
  11. 11.
    Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)CrossRefGoogle Scholar
  12. 12.
    Kanade, T., Tian, Y., Cohn, J.: Comprehensive database for facial expression analysis. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53. IEEE Computer Society (2000)Google Scholar
  13. 13.
    Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: Proceedings of the 3rd International Conference on Face and Gesture Recognition, pp. 200–205. IEEE Computer Society (1998)Google Scholar
  14. 14.
    Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142. IEEE Computer Society (1994)Google Scholar
  15. 15.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  16. 16.
    Martínez, A., Kak, A.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)CrossRefGoogle Scholar
  17. 17.
    Lee, K.-C., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 684–698 (2005)CrossRefGoogle Scholar
  18. 18.
    Martínez, A., Benavente, R.: The AR face database. CVC Technical Report, vol. 24 (1998)Google Scholar
  19. 19.
    Wang, H., Yan, S., Xu, D., Tang, X., Huang, T.: Trace ratio vs. ratio trace for dimensionality reduction. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  20. 20.
    Leibe, B., Schiele, B.: Analyzing Appearance and Contour Based Methods for Object Categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–415. IEEE Computer Society (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Konstantinos Papachristou
    • 1
  • Anastasios Tefas
    • 1
  • Ioannis Pitas
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations