Skip to main content

(2D)2 DLDA for Efficient Face Recognition

  • Conference paper
Advances in Image and Video Technology (PSIVT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4319))

Included in the following conference series:

  • 1216 Accesses

Abstract

In this paper, a new feature representation technique called 2-directional 2-dimensional direct linear discriminant analysis ((2D)2 DLDA) is proposed. In the case of face recognition, the small sample size problem and need for many coeffficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and two directional image scatter matrix. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)

    MATH  Google Scholar 

  2. Belhumeour, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

    Article  Google Scholar 

  3. Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition. 33(10), 1713–1726 (2000)

    Article  Google Scholar 

  4. Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34(10), 2067–2070 (2001)

    Article  MATH  Google Scholar 

  5. Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE Trans. Neural Networks. 14(1), 117–126 (2003)

    Article  Google Scholar 

  6. Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 84–91 (1994)

    Google Scholar 

  7. Turk, M., Pentalnd, A.: Eigenfaces for recognition. J. Cognitive Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  8. Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)

    Article  Google Scholar 

  9. Wang, L., Wang, X., Zhang, X., Feng, J.: The equivalence of two-dimensional PCA to line-based PCA. Pattern Recognition Letters 26, 57–60 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cho, Du., Chang, Ud., Kim, Kd., Kim, Bh., Lee, Sh. (2006). (2D)2 DLDA for Efficient Face Recognition. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_31

Download citation

  • DOI: https://doi.org/10.1007/11949534_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68297-4

  • Online ISBN: 978-3-540-68298-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics