Skip to main content

Advertisement

Log in

Auto white balance method using a pigmentation separation technique for human skin color

  • Regular Paper
  • Published:
Optical Review Aims and scope Submit manuscript

Abstract

The human visual system maintains the perception of colors of an object across various light sources. Similarly, current digital cameras feature an auto white balance function, which estimates the illuminant color and corrects the color of a photograph as if the photograph was taken under a certain light source. The main subject in a photograph is often a person’s face, which could be used to estimate the illuminant color. However, such estimation is adversely affected by differences in facial colors among individuals. The present paper proposes an auto white balance algorithm based on a pigmentation separation method that separates the human skin color image into the components of melanin, hemoglobin and shading. Pigment densities have a uniform property within the same race that can be calculated from the components of melanin and hemoglobin in the face. We, thus, propose a method that uses the subject’s facial color in an image and is unaffected by individual differences in facial color among Japanese people.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Foster, D.H.: Color constancy. Vision. Res. 51, 674–700 (2011)

    Article  Google Scholar 

  2. Gijsenij, A., Gevers, T., Van De Weijer, J.: Computational color constancy: survey and experiments. Image Process IEEE Trans 20, 2475–2489 (2011)

    Article  ADS  MathSciNet  Google Scholar 

  3. Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin Inst. 310, 1–26 (1980)

    Article  Google Scholar 

  4. Land, E.H.: The retinex theory of color vision. Sci Am 237(6), 108–128 (1977)

    Article  Google Scholar 

  5. Lee, H.-C.: Method for computing the scene-illuminant chromaticity from specular highlights. JOSA A 3, 1694–1699 (1986)

    Article  ADS  Google Scholar 

  6. Tsukada M., Funayama C., Tajima J.: Automatic color preference correction for color reproduction. In: Photonics West 2001-Electronic Imaging, pp. 216–223 (2000)

  7. Tsumura, N., Haneishi, H., Miyake, Y.: Independent-component analysis of skin color image. JOSA A 16, 2169–2176 (1999)

    Article  ADS  Google Scholar 

  8. Tsumura N., Ojima N., Nakaguchi T., Miyake Y. (eds.): Skin Color Separation and Synthesis for E-Cosmetics. In: Signal Processing Techniques for Knowledge Extraction and Information Fusion, pp. 201–220. Springer, Berlin (2008)

  9. Tsumura, N., Ojima, N., Sato, K., Shiraishi, M., Shimizu, H., Nabeshima, H., et al.: Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. ACM Trans Gr (TOG) 22, 770–779 (2003)

    Article  Google Scholar 

  10. Finlayson, G.D., Drew, M.S., Funt, B.V.: Color constancy: generalized diagonal transforms suffice. JOSA A 11, 3011–3019 (1994)

    Article  ADS  Google Scholar 

  11. Funt, B.V., Lewis, B.C.: Diagonal versus affine transformations for color correction. JOSA A 17, 2108–2112 (2000)

    Article  ADS  Google Scholar 

  12. Von Kries J.: Sources of color science, Chromatic adaptation, pp. 109–119 (1970)

  13. West, G., Brill, M.H.: Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy. J. Math. Biol. 15, 249–258 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  14. Klinker, G.J., Shafer, S.A., Kanade, T.: A physical approach to color image understanding. Int. J. Comput. Vision 4, 7–38 (1990)

    Article  Google Scholar 

  15. Shafer, S.A.: Using color to separate reflection components. Color Res Appl 10, 210–218 (1985)

    Article  Google Scholar 

  16. Igarashi T., Nishino K., Nayar S. K.: The appearance of human skin: a survey, In: Foundations and Trends® in Computer Graphics and Vision, vol. 3, pp. 1–95 (2007)

  17. Van Gemert, M., Jacques, S.L., Sterenborg, H., Star, W.: Skin optics. Biomed. Eng. IEEE Trans. 36, 1146–1154 (1989)

    Article  Google Scholar 

  18. Hiraoka, M., Firbank, M., Essenpreis, M., Cope, M., Arridge, S., Van Der Zee, P., et al.: A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy. Phys. Med. Biol. 38, 1859 (1993)

    Article  Google Scholar 

  19. Anderson, R.R., Parrish, J.A.: The optics of human skin. J. Investig Dermatol 77, 13–19 (1981)

    Article  Google Scholar 

  20. Drew M. S., Chen C., Hordley S. D., Finlayson G. D.: Sensor transforms for invariant image enhancement. In: Color and Imaging Conference, pp. 325–330 (2002)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Satomi Tanaka.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tanaka, S., Kakinuma, A., Kamijo, N. et al. Auto white balance method using a pigmentation separation technique for human skin color. Opt Rev 24, 17–26 (2017). https://doi.org/10.1007/s10043-016-0290-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10043-016-0290-y

Keywords

Navigation