Advertisement

Face-Based Illuminant Estimation

  • Simone Bianco
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

In this work we show that it is possible to use skin tones to estimate the illuminant color. We use a face detector to find faces in the scene, and the corresponding skin colors to estimate the chromaticity of the illuminant. The method, that has been presented at CVPR 2012 [1] is based on two observations: first, skin colors tend to form a cluster in the color space, making it a cue to estimate the illuminant in the scene; second, many photographic images are portraits or contain people. If no faces are detected, the input image is processed with a low-level illuminant estimation algorithm automatically selected according to [2]. The method will be demonstrated on a public dataset of images in RAW format [3], and on images acquired live during the demo.

Keywords

Input Image Skin Color Color Space Face Detector Color Constancy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full conference paper text

References

  1. 1.
    Bianco, S., Schettini, R.: Color constancy using faces. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 65–72 (2012)Google Scholar
  2. 2.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Automatic color constancy algorithm selection and combination. Pattern Recognition 43, 695–705 (2010)zbMATHCrossRefGoogle Scholar
  3. 3.
    Gehler, P., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  4. 4.
    Forsyth, D.A.: A novel algorithm for color constancy. International Journal of Computer Vision 5, 5–36 (1990)CrossRefGoogle Scholar
  5. 5.
    Barnard, K.: Improvements to Gamut Mapping Colour Constancy Algorithms. In: Vernon, D. (ed.) ECCV 2000, Part I. LNCS, vol. 1842, pp. 390–403. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Witkin, A.: Scale-space filtering: a new approach to multi-scale description. In: IEEE International Conf. on Acoustics, Speech, and Signal Processing, vol. 9, pp. 150–153 (1984)Google Scholar
  7. 7.
    Gijsenij, A., Gevers, T., van de Weijer, J.: Computational color constancy: survey and experiments. IEEE Transactions on Image Processing 20, 2475–2489 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bianco, S., Cusano, C.: Color Target Localization under Varying Illumination Conditions. In: Schettini, R., Tominaga, S., Trémeau, A. (eds.) CCIW 2011. LNCS, vol. 6626, pp. 245–255. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Simone Bianco
    • 1
  • Raimondo Schettini
    • 1
  1. 1.DISCo - Department of Informatics, Systems and ComunicationUniversity of Milano-BicoccaItaly

Personalised recommendations

Cite paper