A Feature Extraction Method Based on Wavelet Transform and NMFs | SpringerLink
Skip to main content

A Feature Extraction Method Based on Wavelet Transform and NMFs

  • Conference paper
Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

Included in the following conference series:

  • 3076 Accesses

Abstract

In this paper, a feature extraction method is proposed by combining Wavelet Transformation (WT) and Non-negative Matrix Factorization with Sparseness constraints (NMFs) together for normal face images and partially occluded ones. Firstly, we apply two-level wavelet transformation to the face images. Then, the low frequency sub-bands are decomposed according to NMFs to extract either the holistic representations or the parts-based ones by constraining the sparseness of the basis images. This method can not only overcome the the low speed and recognition rate problems of traditional methods such as PCA and ICA, but also control the sparseness of the decomposed matrices freely and discover stable, intuitionistic local characteristic more easily compared with classical non-negative matrix factorization algorithm (NMF) and local non-negative matrix decomposition algorithm (LNMF). The experiment result shows that this feature extraction method is easy and feasible with lower complexity. It is also insensitive to the expression and the partial occlusion, obtaining higher recognition rate. Moreover, the WT+NMFs algorithm is robust than traditional ones when the occlusion is serious.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 14299
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Lee, D.D., Seung, H.S.: Unsupervised Learning by Convex and Conic Coding. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, pp. 515–521. The MIT Press, Massachusetts (1997)

    Google Scholar 

  2. Feng, T., Stan, Z., Li, H.Y.S., Zhang, H.J.: Local Non-negative Matrix Factorization as a Visual Representation. In: 2nd International Conference on Development and Learning, Cambridge, pp. 7695–1459 (2002)

    Google Scholar 

  3. Patrik, O.H.: Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)

    Google Scholar 

  4. Manthalkar, R., Biswas, P.K., Chatterji, B.N.: Rotation and Scale Invariant Texture Features Using Discrete Wavelet Packet Transform. Pattern Recognition Letter 24, 2452–2462 (2003)

    Google Scholar 

  5. Lee, D., Seung, H.S.: Learning the Parts of Objects by Non-negative Matrix Factorization. Nature 1401, 788–791 (1999)

    Google Scholar 

  6. Pu, X.R., Zhang, Y., Zheng, Z.M., Wei, Z., Mao, Y.: Face Recognition Using Fisher Non-negative Matrix Factorization with Sparseness Constraints. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 112–117. Springer, Heidelberg (2005)

    Google Scholar 

  7. Ouyang, Y.B., Pu, X.R., Zhang, Y.: Wavelet-based Non-negative Matrix Factorization with Sparseness Constraints for Face Recognition. Application Research of Computers 10, 159–162 (2006)

    Google Scholar 

  8. Chen, W.G., Qi, F.H.: Learning NMF Representation Using a Hybrid Method Combining Feasible Direction Algorithm and Simulated Annealing. Acta Electronica Sinica 31, 2190–2193 (2003)

    Google Scholar 

  9. Zhang, Z.W., Yang, F., Xia, K.W., Yang, R.X.: Research on Face Recognition Method Based on Wavelet Transform and NMF. Computer Engineering 33, 176–179 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, S., Deng, W., Miao, D. (2008). A Feature Extraction Method Based on Wavelet Transform and NMFs. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87732-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics