Skip to main content
SpringerLink
Log in

A Fast Method of Lighting Estimate Using Multi-linear Algebra

  • Conference paper
Advances in Biometric Person Authentication (SINOBIOMETRICS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3338))

Included in the following conference series:

Abstract

Natural facial images are the composite consequence of multiple factors and illumination is an important one. In many situations, we must normalize the facial image’s illumination or simulate the similar lighting condition; therefore, accurate estimation of the facial image’s lighting is necessary and can help get a good result. Because of its richer representational power, multi-linear algebra offers a potent mathematical framework for analyzing the multifactor structure of image ensembles. We apply multi-linear algebra to obtain a parsimonious representation of facial image ensembles which separates the illumination factor from facial images. With the application of multi-linear algebra, we can avoid not only the use of 3D face model, but also that of the complicated iterative algorithm, thus we obtain a fast and simple method of lighting estimation.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

References

  1. Moses, Y., Adini, Y., Ullman, S.: Face Recognition: The Problem of Compensating for Changes in Illumination Direction. In: Proc. European Conf. Computer Vision, pp. 286–296 (1994)

    Google Scholar 

  2. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination. In: IEEE Conf. on Automatic Face and Gesture Recognition, March 2000, pp. 277–284 (2000) (oral presentation)

    Google Scholar 

  3. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. In: IEEE Trans. on Pattern Analysis and Machine Intelligence, June 2001, pp. 643–660 (2001)

    Google Scholar 

  4. Zhao, W., Chellappa, R.: Illumination-Insensitive Face Recognition Using Symmetric Shape-from-Shading. In: Computer Vision and Pattern Recognition (CVPR 2000), June 13 - 15, vol. 1, p. 1286 (2000)

    Google Scholar 

  5. Shashua, A., Riklin-Raviv, T.: The Quotient Image: Class-Based Re-rendering and Recognition with Varying Illuminations. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23(2) (February 2001)

    Google Scholar 

  6. Alex, M., Vasilescu, O., Terzopoulos, D.: Multilinear Image Analysis for Facial Recognition. In: Proceedings of the International Conference on Pattern Recognition, ICPR 2002 (2002)

    Google Scholar 

  7. Alex, M., Vasilescu, O., Terzopoulos, D.: Multilinear Subspace Analysis of Image Ensembles. In: CVPR, vol. II, pp. 93–99 (2003)

    Google Scholar 

  8. Alex, M., Vasilescu, O., Terzopoulos, D.: Multilinear Analysis of Image Ensembles: TensorFaces. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 447–460. Springer, Heidelberg (2002)

    Google Scholar 

  9. YILMAZ, A., GOKMEN, M.: Eigenhill vs. Eigenface and Eigenedge. Pattern Recognition Journal 34(2001), 181–184 (2000)

    Google Scholar 

  10. Kim, H.-C., Kim, D., Bang, S.Y.: FACE RETRIEVAL USING 1ST- AND 2ND-ORDER PCA MIXTURE MODEL. In: Proc. International Conference on Image Processing (ICIP2002), Rochester, USA, September, pp. 605–608 (2002)

    Google Scholar 

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

    Article  Google Scholar 

  12. Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using LDA-Based Algorithms. IEEE Transactions on Neural Networks 14(1), 195–200 (2003)

    Article  Google Scholar 

  13. Wang, X., Qi, H.: Face Recognition Using Optimal Non-orthogonal Wavelet Basis Evaluated By Information Complexity. In: 16 th International Conference on Pattern Recognition (ICPR 2002), August 11 - 15, vol. 1 (2002)

    Google Scholar 

  14. Liu, C., Wechsler, H.: Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks 14(4), 919–928 (2003)

    Article  Google Scholar 

  15. Zhao, W., Chellappa, R.: Robust Face Recognition using Symmetric Shape-from-Shading., Center for Automation Research, University of Maryland, College Park, Technical Report CAR-TR-919

    Google Scholar 

  16. Belhumeur, P., Kriegman, D.: What Is the Set of Images of an Object under All Possible Illumination Conditions. Int’l J.Computer Vision 28(3), 245–260 (1998)

    Article  Google Scholar 

  17. Nillius, P., Eklundh, J.-O.: Low-Dimensional Representations of Shaded Surfaces under Varying Illumination. In: 2003 Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, Wisconsin, June 18 - 20, vol. II, p. 185 (2003)

    Google Scholar 

  18. Zhang, L., Samaras, D.: Face Recognition Under Variable Lighting using Harmonic Image Exemplars. In: Proc.CVPR 2003, pp. I: 19-25 (2003)

    Google Scholar 

  19. Wang, H., Wang, Y., Wei, H.: Face Representation and Reconstruction under Different Illunimation Conditions. In: Seventh International Conference on Information Visualization (IV 2003), July16 - 18, p. 72 (2003)

    Google Scholar 

  20. Basri, R., Jacobs, D.W.: Lambertian Reflectance and Linear Subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2) (February 2003)

    Google Scholar 

  21. Ramamoorthi, R.: Analytic PCA construction for theoretical analysis of lighting variability, including attached shadows, in a single image of a convex Lambertian object. IEEE PAMI 24(10), 1322–1333 (2002)

    Google Scholar 

  22. Epstein, R., Hallinan, P., Yuille, A.: 5 plus or minus 2 eigenimages suffice: An empirical investigation of lowdimensional lighting models. In: IEEE Workshop on Physics-Based Modeling in Computer Vision, pp. 108–116 (1995)

    Google Scholar 

  23. Hallinan, P.: A low-dimensional representation of human faces for arbitrary lighting conditions. In: CVPR 1994, pp. 995–999 (1994); IJCV 35(3), 203–222 (1999).

    Google Scholar 

  24. Qing, L., Shan, S., Gao, W.: Face De-lighting under Natural Illumination. Advances in Biometrics (2), 43

    Google Scholar 

  25. Sim, T., Kanade, T.: Combining Models and Exemplars for Face Recognition: an Illumination Example. In: Proc. Of Workshop on Models versus Exemplars in Computer Vision, CVPR 2001 (2001)

    Google Scholar 

  26. Kolda, T.G.: Orthogonal tensor decompositions. SIAM Journal on Matrix Analysis and Applications 23(1), 243–255 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  27. Qing, L., Shan, S., Gao, W.: Illumination Alignment for Face Image with Ratio Image. Advances in Biometrics (2), 106

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The State Key Laboratory of Intelligent Technology and System, Electronic Engineering Department, Tsinghua University, Beijing, 100084, China

    Yuequan Luo & Guangda Su

Authors
  1. Yuequan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangda Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,  

    Stan Z. Li

  2. Department of Electronics & Communication Engineering, Sun Yat-Sen University, Guangzhou, China

    Jianhuang Lai

  3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  4. Center of Computer Vision, School of Mathematics and Computing Science, Sun Yat-sen University, 510275, Guangzhou, China

    Guocan Feng

  5. School of Computer Science and Engineering, Beihang University, Beijing, China

    Yunhong Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Luo, Y., Su, G. (2004). A Fast Method of Lighting Estimate Using Multi-linear Algebra. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30548-4_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24029-7

  • Online ISBN: 978-3-540-30548-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation