Skip to main content
SpringerLink
Log in

Fuzzy reasoning model to improve face illumination invariance

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Enhancing facial images captured under different lighting conditions is an important challenge and a crucial component in the automatic face recognition systems. This work tackles illumination variation challenge by proposing a new face image enhancement approach based on Fuzzy theory. The proposed Fuzzy reasoning model generates an adaptive enhancement which corrects and improves non-uniform illumination and low contrasts. The FRM approach has been assessed using four blind-reference image quality metrics supported by visual assessment. A comparison to six state-of-the-art methods has also been provided. Experiments are performed on four public data sets, namely Extended Yale-B, Mobio, FERET and Carnegie Mellon University Pose, Illumination, and Expression, showing very interesting results achieved by our approach.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Agaian, S., Roopaei, M., Shadaram, M., Bagalkot, S.S.: Bright and dark distance-based image decomposition and enhancement. In: 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, pp. 73–78 (2014). doi:10.1109/IST.2014.6958449

  2. Agaian, S.S., Panetta, K., Grigoryan, A.M.: A new measure of image enhancement. In: IASTED International Conference on Signal Processing & Communication, pp. 19–22. Citeseer (2000)

  3. Agaian, S.S., Panetta, K., Grigoryan, A.M.: Transform-based image enhancement algorithms with performance measure. IEEE Trans. Image Process. 10(3), 367–382 (2001). doi:10.1109/83.908502

    Article  MATH  Google Scholar 

  4. Aouache, M., Hussain, A., Zulkifley, M.A., Wan Zaki, D.W.M., Husain, H., Abdul Hamid, H.B.: Anterior osteoporosis classification in cervical vertebrae using fuzzy decision tree. Multimed Tools Appl, 1–35 (2017). doi:10.1007/s11042-017-4468-5

  5. Aouache, M., Oulefki, A., Bengherabi, M., Boutellaa, E., Almahdi Algaet, M.: Towards nonuniform illumination face enhancement via adaptive contrast stretching. Multimed Tools Appl, 1–39 (2017). doi:10.1007/s11042-017-4665-2

  6. Arriaga-Garcia, E.F., Sanchez-Yanez, R.E., Garcia-Hernandez, M.G.: Image enhancement using bi-histogram equalization with adaptive sigmoid functions. In: 2014 International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 28–34 (2014). doi:10.1109/CONIELECOMP.2014.6808563

  7. Asmare, M.H., Asirvadam, V.S., Hani, A.F.M.: Image enhancement based on contourlet transform. Signal Image Video Process. 9(7), 1679–1690 (2015). doi:10.1007/s11760-014-0626-7

    Article  Google Scholar 

  8. Banerjee, P.K., Datta, A.K.: Band-pass correlation filter for illumination- and noise-tolerant face recognition. Signal Image Video Process. 11(1), 9–16 (2017). doi:10.1007/s11760-016-0882-9

    Article  Google Scholar 

  9. Best-Rowden, L., Jain, A.K.: Automatic face image quality prediction. CoRR. arxiv:1706.09887

  10. Chang, S.J., Li, S., Andreasen, A., Sha, X.Z., Zhai, X.Y.: A reference-free method for brightness compensation and contrast enhancement of micrographs of serial sections. PloS ONE 10(5), e0127855 (2015)

    Article  Google Scholar 

  11. Du, S., Ward, R.: Wavelet-based illumination normalization for face recognition. In: IEEE International Conference on Image Processing 2005, vol. 2, pp. II-954–II-957 (2005). doi:10.1109/ICIP.2005.1530215

  12. Eramian, M., Mould, D.: Histogram equalization using neighborhood metrics. In: The 2nd Canadian Conference on Computer and Robot Vision (CRV’05), pp. 397–404. IEEE (2005)

  13. Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  14. Hasikin, K., Mat Isa, N.A.: Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. Signal Image Video Process. 8(8), 1591–1603 (2014). doi:10.1007/s11760-012-0398-x

    Article  Google Scholar 

  15. Hasikin, K., Mat Isa, N.A.: Adaptive fuzzy intensity measure enhancement technique for non-uniform illumination and low-contrast images. Signal Image Video Process. 9(6), 1419–1442 (2015). doi:10.1007/s11760-013-0596-1

    Article  Google Scholar 

  16. Kryszczuk, K., Drygajlo, A.: On face image quality measures. In: Proceedings of the 2nd Workshop on Multimodal User Authentication (2006)

  17. Lee, K.C., Ho, J., Kriegman, D.J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)

    Article  Google Scholar 

  18. Lim, S.H., Mat Isa, N.A., Ooi, C.H., Toh, K.K.V.: A new histogram equalization method for digital image enhancement and brightness preservation. Signal Image Video Process. 9(3), 675–689 (2015). doi:10.1007/s11760-013-0500-z

    Article  Google Scholar 

  19. McCool, C., Marcel, S., Hadid, A., Pietikainen, M., Matejka, P., Cernocky, J., Poh, N., Kittler, J., Larcher, A., Levy, C., Matrouf, D., Bonastre, J.F., Tresadern, P., Cootes, T.: Bi-modal person recognition on a mobile phone: using mobile phone data. In: IEEE ICME Workshop on Hot Topics in Mobile Multimedia (2012)

  20. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16, 295–306 (1998)

    Article  Google Scholar 

  21. Pizer, S., Amburn, E., Austin, J., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Gr. Image Process. 39(3), 355–368 (1987)

    Article  Google Scholar 

  22. Poddar, S., Tewary, S., Sharma, D., Karar, V., Ghosh, A., Pal, S.K.: Non-parametric modified histogram equalisation for contrast enhancement. IET Image Process. 7(7), 641–652 (2013). doi:10.1049/iet-ipr.2012.0507

    Article  Google Scholar 

  23. Roopaei, M., Agaian, S., Shadaram, M., Hurtado, F.: Cross-entropy histogram equalization. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 158–163 (2014). doi:10.1109/SMC.2014.6973900

  24. Sao, A.K., Yegnanarayana, B.: On the use of phase of the fourier transform for face recognition under variations in illumination. Signal Image Video Process. 4(3), 353–358 (2010). doi:10.1007/s11760-009-0125-4

    Article  MATH  Google Scholar 

  25. Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4), 2475–2480 (2010). doi:10.1109/TCE.2010.5681130

    Article  Google Scholar 

  26. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (pie) database. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, FGR’02, pp. 53–58. IEEE Computer Society, Washington, DC, USA (2002)

  27. Tizhoosh, H.R.: Fuzzy image enhancement: An overview. In: Kerre, E.E., Nachtegael, M. (eds.) Fuzzy Techniques in Image Processing, pp. 137–171. Physica-Verlag HD, Heidelberg (2000). doi:10.1007/978-3-7908-1847-5

  28. Yager, R.R., Zadeh, L.A.: Fuzzy Sets, Neural Networks and Soft Computing, 1st edn. Wiley, New York (1994)

    MATH  Google Scholar 

  29. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC–3(1), 28–44 (1973). doi:10.1109/TSMC.1973.5408575

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003). doi:10.1145/954339.954342

    Article  Google Scholar 

  31. Zhou, Y., Panetta, K., Agaian, S.: Human visual system based mammogram enhancement and analysis. In: 2010 2nd International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 229–234. IEEE (2010)

Download references

Acknowledgements

This research received funding from the Algerian Ministry of Higher Education and Scientific Research (AMHESR) via the National Research Fund project (AVVISA-FNR-2013-2016/003). The authors are with the Centre de Dévelopment des Technologies Avancées (CDTA).

Author information

Authors and Affiliations

  1. Centre de Développement des Technologies Avancées, Baba Hassen, Algeria

    Adel Oulefki, Aouache Mustapha, Elhocine Boutellaa, Messaoud Bengherabi & Ahmed Amine Tifarine

  2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland

    Elhocine Boutellaa

Authors
  1. Adel Oulefki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aouache Mustapha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elhocine Boutellaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Messaoud Bengherabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ahmed Amine Tifarine
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adel Oulefki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oulefki, A., Mustapha, A., Boutellaa, E. et al. Fuzzy reasoning model to improve face illumination invariance. SIViP 12, 421–428 (2018). https://doi.org/10.1007/s11760-017-1174-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-017-1174-8

Keywords

Search

Navigation