Skip to main content
SpringerLink
Log in

Evaluation of Illumination Compensation Approaches for ELGBPHS

  • Conference paper
Computer Recognition Systems 4

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 95))

  • 1309 Accesses

Abstract

Various environmental conditions like pose variations, scale, noise and illumination changes cause matching problems for face recognition algorithms due to the fact that inappropriate data from images is extracted and consequently the recognition rate suffers. In the worst case, persons who should be accepted are rejected and vice versa. Enhanced Local Gabor Binary Patterns Histogram Sequence (ELGBPHS) is considered as an advanced and robust face recognition method. In this paper we evaluated if state-of-the-art illumination compensation approaches can further improve the performance of ELGBPHS. The paper outlines if it is worth to additionally implement preprocessing steps with the increasing complexity and cost. Therefore tests were performed to check if the recognition rate improves if applying preprocessing steps and adjusting essential parameters. Multi-Scale-Retinex, Histogram Equalization, 2D discrete Wavelet-Transformation and one approach combining Gamma Correction, Difference of Gaussian Filtering and Contrast Equalization (TT) were implemented and evaluated.

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 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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. Acharya, T., Ray, A.K.: Image Processing: Principles and Applications. Wiley-Interscience, Hoboken (2005)

    Google Scholar 

  2. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Bolme, D.: Elastic bunch graph matching. Master’s thesis, Colorado State University (2003)

    Google Scholar 

  4. Du, S., Ward, R.: Wavelet-based illumination normalization for face recognition. In: Proceedings of the 2005 IEEE International Conference on Image Processing (ICIP 2005), pp. 954–957. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  5. Gross, R., Baker, S., Matthews, I., Kanade, T.: Face Recognition Across Pose and Illumination. In: Handbook of Face Recognition, pp. 193–216. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Intel. Opencv library (2010), http://opencv.willowgarage.com/wiki/

  7. Jobson, D., Rahman, Z., Woodell, G.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  8. Kela, N., Rattani, A., Gupta, P.: Illumination invariant elastic bunch graph matching for efficient face recognition. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), p. 42. IEEE Computer Society, Los Alamitos (2006)

    Chapter  Google Scholar 

  9. Lades, M., Vorbruggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993)

    Article  Google Scholar 

  10. Phillipsa, P.J., Wechsler, H., Huang, J., Rauss, P.: The feret database and evaluation procedure for face recognition algorithms. Image Vision Comput. 16(5), 295–306 (1998)

    Article  Google Scholar 

  11. Rahman, Z., Jobson, D.J., Woodell, G.A.: Retinex processing for automatic image enhancement. J. of Electron. Imaging 13(1), 100–110 (2004)

    Article  Google Scholar 

  12. Serrano, A., de Diego, I.M., Conde, C., Cabello, E.: Recent advances in face biometrics with gabor wavelets: A review. Pattern Recogn. Lett. 31(5), 372–381 (2010)

    Article  Google Scholar 

  13. Sinha, P., Balas, B., Ostrovsky, Y., Russell, R.: Face recognition by humans: Nineteen results all computer vision researchers should know about. Proceedings of the IEEE 94, 1948–1962 (2006)

    Article  Google Scholar 

  14. Su, Y., Shan, S., Chen, X., Gao, W.: Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans. Image Process. 18(8), 1885–1896 (2009)

    Article  Google Scholar 

  15. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  Google Scholar 

  16. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  17. Wiskott, L., Fellous, J.M., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. 19(7), 775–779 (1997)

    Article  Google Scholar 

  18. Xie, S., Shan, S., Chen, X., Chen, J.: Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans. Image Process. 19(5), 1349–1361 (2010)

    Article  Google Scholar 

  19. Zhang, B., Shan, S., Chen, X., Gao, W.: Histogram of gabor phase patterns (hgpp): A novel object representation approach for face recognition. IEEE Trans. Image Process. 16(1), 57–68 (2007)

    Article  MathSciNet  Google Scholar 

  20. Zhang, W., Shan, S., Qing, L., Chen, X., Gao, W.: Are gabor phases really useless for face recognition? Pattern Anal. Appl. 12(3), 301–307 (2009)

    Article  Google Scholar 

  21. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of IT-Security Research, St. Poelten University of Applied Sciences, Matthias Corvinus-Strasse 15, 3100, St. Poelten, Austria

    Matthias Fischer, Marlies Rybnicek & Christoph Fischer

Authors
  1. Matthias Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marlies Rybnicek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk , Marek Kurzyński  & Andrzej Żołnierek ,  & 

  2. Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fischer, M., Rybnicek, M., Fischer, C. (2011). Evaluation of Illumination Compensation Approaches for ELGBPHS. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20320-6_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20319-0

  • Online ISBN: 978-3-642-20320-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation