access icon free Colour balancing using sclera colour

© The Institution of Engineering and Technology
Received 21/02/2017, Accepted 29/10/2017, Revised 01/10/2017, Published 03/11/2017

Colour balancing is an image processing step employed in image signal processing pipeline to adjust colouration of images captured under different illuminations. Most of the existing colour balancing methods that make use of human faces and facial features use skin colour to estimate the chromaticity of the illuminant. This study examines how colour balancing can be performed exploiting the sclera colour extracted from the face automatically detected in the image. Sclera colour can provide enough and correct information to estimate the scene illuminant and reliably perform automatic colour balancing for face images. Experimental results suggest that, in terms of accuracy, the proposed method outperforms most other colour constancy methods on the experimental dataset collected as part of this research, which is a significant result.

Inspec keywords: image colour analysis; image segmentation

Other keywords: scene illuminant estimation; image signal processing; image signal processing pipeline; automatic colour balancing; face images; sclera colour; chromatic adaptation; eye region detection; sclera segmentation

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

References

    1. 1)
      • 13. Bianco, S., Schettini, R.: ‘Adaptive color constancy using faces’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (8), pp. 1505–1518.
    2. 2)
      • 5. Kim, S.J., Oh, S.W.: ‘Approaching the computational color constancy as a classification problem through deep learning’, Pattern Recognit., 2017, 61, pp. 405–416.
    3. 3)
      • 18. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. IEEE Conf. Vision and Pattern Recognition (CVPR), 2001, pp. 511–518.
    4. 4)
      • 7. Chakrabarti, A.: ‘Color constancy by learning to predict chromaticity from luminance’. Proc. Neural Information Processing Systems (NIPS), 2015, pp. 163–171.
    5. 5)
      • 1. Lam, E.Y., Fung, G.S.K.: ‘Automatic white balancing in digital photography’, in Lukac, R. (Ed.): ‘Single-sensor imaging: methods and applications for digital cameras’ (CRC Press, Boca Raton, FL, 2008, 1st edn.), pp. 267–294.
    6. 6)
      • 2. Gijsenij, A., Gevers, T., van de Weijer, J.: ‘Computational color constancy: survey and experiments’, IEEE Trans. Image Process., 2011, 20, (9), pp. 2475–2489.
    7. 7)
      • 11. Bianco, S., Schettini, R.: ‘Color constancy using faces’. Proc. IEEE Conf. Vision and Pattern Recognition (CVPR), 2012, pp. 65–72.
    8. 8)
      • 29. Finlayson, G.D., Hordley, S.D., Morovic, P.: ‘Colour constancy using the chromagenic constraint’. Proc. IEEE Conf. Vision and Pattern Recognition (CVPR), 2005, vol. 1, pp. 1079–1086.
    9. 9)
      • 28. Hordley, S.D., Finlayson, G.: ‘Re-valuating colour constancy algorithms’. Proc. Conf. Vision and Pattern Recognition (ICPR), 2004, pp. 76–79.
    10. 10)
      • 6. Cheng, D., Price, B., Cohen, S., et al: ‘Effective learning-based illuminant estimation using simple features’. Proc. IEEE Conf. Vision and Pattern Recognition, 2015, pp. 1000–1008.
    11. 11)
      • 20. Akkaynak, D., Treibitz, T., Xiao, B., et al: ‘Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration’, J. Opt. Soc. Am. A – OSA, 2014, 31, (2), pp. 312–321.
    12. 12)
      • 16. Bianco, S.: ‘Color constancy using single colors’. Proc. Computer Vision – ECCV 2012. Workshops and Demonstrations, 2012, pp. 390–400.
    13. 13)
      • 15. von Kries, J.: ‘Chromatic adaptation’, Festchrift der Albrecht-Ludwigs-Universitat, 1902 [oTranslation: D.L. MacAdam, ‘Colorimetry – fundamentals’, SPIE Milestone Series, vol. MS 77, 1993].
    14. 14)
      • 12. Crichton, S., Pichat, J., Mackiewicz, M., et al: ‘Skin chromaticity gamuts for illumination recovery’. Proc. Conf. on Colour in Graphics, Imaging and Vision (CGIV), 2012, pp. 266–271.
    15. 15)
      • 25. van de Weijer, J., Gevers, T., Gijsenij, A.: ‘Edge-based color constancy’, IEEE Trans. Image Process., 2007, 16, (9), pp. 2207–2214.
    16. 16)
      • 26. Banic, N., Loncaric, S.: ‘Color rabbit guiding the distance of local maximums in illumination estimation’. Proc. IEEE Digital Signal Processing (DSP), 2014, pp. 345–350.
    17. 17)
      • 19. Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: ‘Face detection in color images’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (5), pp. 696–706.
    18. 18)
      • 21. Land, E. H.: ‘The retinex theory of color vision’, Sci. Am., 1977, 237, (6), pp. 108–128.
    19. 19)
      • 4. Barron, J. T.: ‘Convolutional color constancy’. Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 379–387.
    20. 20)
      • 3. Bianco, S., Cusano, C., Schettini, R.: ‘Color constancy using CNNs’. Proc. IEEE Conf. Vision and Pattern Recognition, 2015, pp. 81–89.
    21. 21)
      • 24. Finlayson, G.D., Trezzi, E.: ‘Shades of gray and colour constancy’, Color and Imaging Conf., 2004, vol. 2004, pp. 37–41.
    22. 22)
      • 27. Banić, N., Lončarić, S.: ‘Using the random sprays retinex algorithm for global illumination estimation’. Proc. Croatian Computer Vision Workshop (CCVW), 2013, pp. 3–7.
    23. 23)
      • 23. Buchsbaum, G.: ‘A spatial processor model for object colour perception’, J. Franklin Inst., 1980, 310, (1), pp. 1–26.
    24. 24)
      • 22. Banic, N., Loncaric, S.: ‘Improving the white patch method by subsampling’. Proc. IEEE Int. Conf. on Image Processing (ICIP), 2014, pp. 605–609.
    25. 25)
      • 9. Gijsenij A., Gevers, T: ‘Color constancy using natural image statistics and scene semantics’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (4), pp. 687–698.
    26. 26)
      • 10. Fairchild, M.D.: ‘Chromatic adaptation to image displays’. Technical Association of Graphic Arts, 1992, 2, pp. 803–824.
    27. 27)
      • 14. Nishino, K., Nayar, S.K.: ‘Eyes for relighting’, ACM Trans. Graph., 2004, 23, (3), pp. 704–711.
    28. 28)
      • 8. van de Weijer, J., Schmid, C., Verbeek, J.: ‘Using high-level visual information for color constancy’. Proc. IEEE Int. Conf. Computer Vision, 2007, pp. 1–8.
    29. 29)
      • 17. Sumner, R.: ‘Processing RAW images in MATLAB’, unpublished.
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