Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2009: Advances in Neural Networks – ISNN 2009 pp 429–438Cite as

DT-CWT Feature Structure Representation for Face Recognition under Varying Illumination Using EMD

  • Conference paper

  • 2258 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

Abstract

We introduce a method for illumination detection and removal techinique using Empirical Mode Decomposition (EMD) to decompose subimages of Dual-Tree Complex Wavelet Transform (DT-CWT). The subimages are reconstructed without illumination distortion components for face recognition. Compared with others, this method has the following advantages: it can be directly applied without any prior information; it has perfectly reconstruction ability because of DT-CWT in low frequency. Experiments are carried out upon the Yale B and CMU PIE face databases, and the results demonstrate that the proposed method shows satisfactory recognition rates under varying illumination conditions.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Phillips, P.J., Grother, P., Micheals, R., Blackburn, D.M., Tabassi, E., Bone, M.: Face Recognition Vendor Test 2002, Evaluation report. Technical Report IR 6965, NIST (2003)

    Google Scholar 

  2. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Jaesik, M., Worek, W.: Overview of the Face Recognition Grand Challenge. In: CVPR 1, pp. 947–954 (2005)

    Google Scholar 

  3. Adini, Y., Moses, Y., Ullman, S.: Face Recognition: The Problem of Compensating for Changes in Illumination Direction. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 721–732 (1997)

    Article  Google Scholar 

  4. Belhumeur, 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, 711–720 (1997)

    Article  Google Scholar 

  5. Chen, T., Hsu, Y.J., Liu, X., Zhang, W.: Principle Component Analysis and Its Variants for Biometrics. In: Image Processing Proceedings International Conference, vol. 1, pp. 61–64 (2002)

    Google Scholar 

  6. Gross, R., Matthews, I., Baker, S.: Eigen Light-Fields and Face Recognition Across Pose. In: 5th IEEE International Conference Automatic Face and Gesture Recognition Proceedings, pp. 1–7 (2002)

    Google Scholar 

  7. He, X., Niyogi, P.: Locality Preserving Projections. In: Advances in Neural Information Processing Systems, vol. 16, pp. 153–160 (2003)

    Google Scholar 

  8. Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1615–1618 (2003)

    Article  Google Scholar 

  9. Kingsbury, N.G.: The Dual-tree Complex Wavelet Transform: a New Efficient Tool for Image Restoration and Enhancement. In: Proc. European Signal Processing Conf., pp. 319–322 (1998)

    Google Scholar 

  10. Liu, C.J., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. Image Processing 11, 467–476 (2002)

    Article  Google Scholar 

  11. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis. Proc. Royal Society of London A 454, 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  12. Kokiopoulou, E., Saad, Y.: Orthogonal Neighborhood Preserving Projections. In: IEEE Int. Conf. on Data Mining, pp. 1–8 (2005)

    Google Scholar 

  13. Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  14. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)

    Article  Google Scholar 

  15. Flandrin, P., Goncalves, P., Rilling, G.: Empirical Mode Decomposition as a Filter Bank. IEEE Signal Processing Letters 11, 112–114 (2004)

    Article  Google Scholar 

  16. Turk, M.A., Pentland, A.P.: Face Recognition Using Eigenfaces. In: Computer Vision and Pattern Recognition Proceedings IEEE Computer Society Conference, pp. 586–591 (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Engineering, Guangdong Univeristiy of Technology, Guangzhou, 510006, China

    Yuehui Sun

  2. College of Computer Science, Shaoguan University, ShaoGuan, 512005, China

    Di Zhang

Authors
  1. Yuehui Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Di Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, SD 57701, Rapid City, USA

    Nian Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sun, Y., Zhang, D. (2009). DT-CWT Feature Structure Representation for Face Recognition under Varying Illumination Using EMD. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01513-7_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation