On the Influence of Illumination Quality in 2D Facial Recognition

  • Ángel Sánchez
  • José F. Vélez
  • A. Belén Moreno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)


Detecting automatically whether a facial image is greatly affected or not by the illumination conditions, allows us in some cases to discard those deteriorated images for further recognition tasks or, in other cases, to apply a preprocessing method only to the images that really need it. With this aim, our paper presents a study on the isolated influence of illumination quality of 2D images in facial recognition. First, a fuzzy inference system is designed to be as an objective and automatic method to evaluate the illumination quality of facial patterns. Then, we estimate the best recognition result for the same images using different image classification methods. By combining both the illumination quality with the corresponding recognition results for same face images, and computing the regression line of this set of patterns, we detect a nearly-linear regression trend between illumination quality and recognition rate for the images tested. This result can then be used as a quality measure of patterns in 2D facial recognition, and also for deciding whether it is worth or not using these facial images in recognition tasks.


Illumination quality Facial variations 2D facial recognition Anthropometric feature points Fuzzy inference system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abaza, A., Harrison, M.A., Bourlai, T., Ross, A.: Design and evaluation of photometric image quality measures for effective face recognition. IET Biometrics 3, 314–324 (2014)CrossRefGoogle Scholar
  2. 2.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2D and 3D face recognition: A survey. Pattern Recognition Letters 28, 1885–1906 (2007)CrossRefGoogle Scholar
  3. 3.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 721–732 (1997)CrossRefGoogle Scholar
  4. 4.
    Georghiades, A., Belhumeur, P.N., Kriegman, D.: From Few to Many: Illumination Cone Models for Face Recognition under Variable lighting and Pose. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 643–660 (2001)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall (2008)Google Scholar
  6. 6.
    Gross, R., Baker, S., Matthews, I., Kanade, T.: Face Recognition across Pose and Illumination. In: Li, S.Z., Jain, A.K. (eds.) Handbook of Face Recognition, pp. 193–216. Springer (2004)Google Scholar
  7. 7.
    Koenen, R.: MPEG-4 Overview, International Organisation for Standarisation, ISO/IEC JTC1/SC29/WG11 No. 4668 (2002),
  8. 8.
    Leekwijck, W.V., Kerre, E.E.: Defuzzification: criteria and classification. Fuzzy Sets and Systems 108, 159–178 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Park, Y.K., Min, B.C., Kim, J.K.: A New Method of Illumination Normalization for Robust Face Recognition. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 38–47. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Phillips, P.J., et al.: Overview of the face recognition grand challenge. In: Hebert, M., Kriegman, D. (eds.) Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 947–954 (2005)Google Scholar
  11. 11.
    Rizo-Rodrıguez, D., Mendez-Vazquez, H., Garcıa-Reyes, E.: An Illumination Quality Measure for Face Recognition. In: Ercil, A. (ed.) Proc. 20th International Conference on Pattern Recognition (ICPR 2010), pp. 1477–1480. IEEE Press (2010)Google Scholar
  12. 12.
    Sheikh, H.R., Sabir, M.F., Bovik, A.C.: An evaluation of recent full reference image quality assessment algorithms. IEEE Trans. on Image Processing 15, 3440–3451 (2006)CrossRefGoogle Scholar
  13. 13.
    Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier Publishing Company (1985)Google Scholar
  14. 14.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. Image Processing 13, 600–612 (2004)CrossRefGoogle Scholar
  15. 15.
    Zhang, J., Xie, X.: A study on the effective approach to illumination-invariant face recognition based on a single image. In: Zheng, W.-S., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds.) CCBR 2012. LNCS, vol. 7701, pp. 33–41. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Zou, X., Kittler, J., Messer, K.: Illumination Invariant Face Recognition: A Survey. In: Bowyer, K.W. (ed.) Proc. First IEEE Intl. Conf. on Biometrics: Theory, Applications, and Systems (BTAS 2007), pp. 1–8 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ángel Sánchez
    • 1
  • José F. Vélez
    • 1
  • A. Belén Moreno
    • 1
  1. 1.Departamento de Informática y EstadísticaUniversidad Rey Juan CarlosMóstolesSpain

Personalised recommendations