Skip to main content

Wavelet Based SDA for Face Recognition

  • Conference paper
Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8836))

Included in the following conference series:

  • 4417 Accesses

Abstract

Semi-supervised discriminant analysis (SDA) is a popular semi-supervise learning technique for limited labelled training sample problem in face recognition. However, SDA resides in the illumination variations and noise of the face features. Hence, SDA exposes the illumination variations and noise when constructing the optimal projection. It could affect the projection, leading to poor performance. In this paper, an enhanced SDA, namely Wavelet SDA, is proposed. This proposed technique is to resolve the problem of intra-class variations due to illumination variations and noise on image data. The robustness of the proposed technique is evaluated using three well-known face databases, i.e. ORL, FERET and FRGC. Empirical results validated the good effects of wavelet transform on SDA, leading to better recognition performance.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

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. Academic Press (1990)

    Google Scholar 

  2. Carreira-Perpinan, M.A.: A review of dimension reduction techniques.: Department of Computer Science. University of Sheffield. Tech. Rep. CS-96-09, 1-69 (1997)

    Google Scholar 

  3. Jolliffe, I.: Principal component analysis. John Wiley & Sons, Ltd. (2005)

    Google Scholar 

  4. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  5. Lone, M.A., Zakariya, S.M., Ali, R.: Automatic Face Recognition System by Combining Four Individual Algorithms. In: 2011 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 222–226. IEEE (2011)

    Google Scholar 

  6. Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–7. IEEE (2007)

    Google Scholar 

  7. Zhu, X., Lafferty, J., Rosenfeld, R.: Semi-supervised learning with graphs (Doctoral dissertation, Carnegie Mellon University, Language Technologies Institute, School of Computer Science) (2005)

    Google Scholar 

  8. Qiao, L., Zhang, L., Chen, S.: An empirical study of two typical locality preserving linear discriminant analysis methods. Neurocomputing 73(10), 1587–1594 (2010)

    Article  Google Scholar 

  9. Al Muhit, A., Islam, M.S., Othman, M.: VLSI implementation of discrete wavelet transform (DWT) for image compression. In: 2nd International Conference on Autonomous Robots and Agents, vol. 4(4), pp. 421–433 (2004)

    Google Scholar 

  10. Lai, J.H., Yuen, P.C., Feng, G.C.: Face recognition using holistic Fourier invariant features. Pattern Recognition 34(1), 95–109 (2001)

    Article  MATH  Google Scholar 

  11. Naik, S., Patel, N.: Single Image Super Resolution in Spatial and Wavelet Domain. International Journal of Multimedia & Its Applications 5(4) (2013)

    Google Scholar 

  12. Graps, A.: An introduction to wavelets. IEEE Computational Science & Engineering 2(2), 50–61 (1995)

    Article  Google Scholar 

  13. Friedman, J.H.: Regularized discriminant analysis. Journal of the American Statistical Association 84(405), 165–175 (1989)

    Article  MathSciNet  Google Scholar 

  14. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 7, 2399–2434 (2006)

    MathSciNet  MATH  Google Scholar 

  15. Chung, F.R.: Spectral graph theory. In: CBMS Regional Conference Series in Mathematics, vol. 92 (1996)

    Google Scholar 

  16. ORL face Database. AT&T Laboratories, Cambridge, U. K, http://www.uk.research.att.com/facedatabase.html

  17. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  18. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 947–954. IEEE (2005)

    Google Scholar 

  19. Larose, D.T.: k-Nearest Neighbor Algorithm. Discovering Knowledge in Data: An Introduction to Data Mining, pp. 90–106 (2005)

    Google Scholar 

  20. Steinwart, I., Christmann, A.: Support vector machines. Springer (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Ling, G.F., Han, P.Y., Ping, L.Y., Yin, O.S., Kiong, L.C. (2014). Wavelet Based SDA for Face Recognition. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_76

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12643-2_76

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12642-5

  • Online ISBN: 978-3-319-12643-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics