Simon fraser university computational vision lab lilong shi brian funt and tim lee
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Simon Fraser University Computational Vision Lab Lilong Shi, Brian Funt and Tim Lee. Studies in Appearance of Skin. Overview. Studies of factors affecting skin colour Simple and linear model of skin Modelling Skin appearance under lights Applications:

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Studies in Appearance of Skin

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Simon fraser university computational vision lab lilong shi brian funt and tim lee

Simon Fraser University

Computational Vision Lab

Lilong Shi, Brian Funt and Tim Lee

Studies in Appearance of Skin


Overview

Overview

  • Studies of factors affecting skin colour

  • Simple and linear model of skin

  • Modelling Skin appearance under lights

  • Applications:

    • Estimate melanin and hemoglobin concentrations

    • Correct imaged skin tones for lighting conditions


Applications

Applications

Skin tone correction

Tone correction

Preserve melanin

Melanin/Hemoglobin separation


Appearance of human skin

Appearance of Human Skin

  • Appearance of human skin determined by

    • Biological factors

      • pigmentation, blood microcirculation, roughness, etc..

    • Viewing conditions

      • Inducing lights

    • Acquisition devices

      • Cones in retina, RGB sensors of CCD digital cameras


Schematic model of human skin

Schematic Model of Human Skin

  • Two-layered Skin Model [2]

    • Epidermis Layer: melanin absorbance

    • Dermis Layer: hemoglobin absorbance

  • A layer has properties of an optical filter


Biological factors

Biological Factors

  • Various skin colour <= melanin + hemoglobin

    • Genetic: Race

    • Temporary:

      • Exposure to UV

      • Hot bath

  • Mixture varying by 2 independent factors

  • Analyse melanin and hemoglobin factors


Blind source separation

Blind Source Separation

  • Estimate melanin and hemoglobin concentration

  • Independent Component Analysis (ICA)

    • Statistical technique for revealing “hidden” factors

    • To “unmix” or “separate” signals composed of multiple sources

    • Independent and linear mixing

    • Related to Eigen-vector analysis


Independent component analysis ica

Independent Component Analysis (ICA)

Original Source Signals

Mixing

Observed Signals

70%

v1

0%

20%

80%

30%

s1

s2

100%

v2

v3

s × A = v


Independent component analysis ica1

Independent Component Analysis (ICA)

Melanin

Melanin

Hemoglobin

Hemoglobin

Skin samples


A example of skin basis

A Example of Skin basis

  • Typical skin spectrum

    • Visible wavelength 400nm – 700nm

  • Extract skin bases from observed spectrum by ICA

(left) 33 skin spectrum after normalization; (right) two independent basis spectrum – the melanin and hemoglobin, and the spectrum of chromophores other than melanin and hemoglobin pigments.

ICA


Linear model of skin

Linear Model of Skin

  • Arbitrary skin spectrum can be approximated

are variables

constru


Signal from digital camera

Signal from Digital Camera

  • Human vision

    • 3 types of Photoreceptors

      • L, M and S Cones

  • Digital Cameras

    • 3 sensors

      • Red, Green, and Blue

  • Reflectance spectrum recorded by 3 sensors => three values (R, G, B) for a skin colour


Skin model in camera space

Skin Model in Camera Space

Given a pixel from skin, compute by projecting log(R,G,B) onto

Possible skin colours lie within plane


Real image result

Real Image Result

Melanin Image

Hemoglobin Image

Input Image [3]


Results on 33 skin spectra

Results on 33 Skin Spectra

- Inverse melanin concentration

- Inverse hemoglobin concentration


Modelling skin illumination

Modelling Skin + Illumination

  • Skin appearance greatly affected by lights

  • Reveal true skin colour by removing illum.

  • Common lights blackbody radiation

    • e.g. tungsten/halogen lamps, sunrise/sunset, etc

    • Varying colour temperature T

      • Redish -> white -> bluish


Skin illumination model

Skin-Illumination Model

  • Colour: illumination times reflectance

  • In log space, multiplication => addition:

Illum. basis


Simplified skin illum model

Simplified Skin-Illum Model

  • In practice

  • Drop hemoglobin basis

    • Small angle between Illum and hemoglobin axes

  • Ignore brightness

  • Skin colour varying by T and


  • Result based on synthesized data

    Result based on Synthesized Data

    • 384 real skin reflectances times

    • 67 real light sources

      • => 25728 samples


    Result of tone correction

    Result of Tone Correction

    • Skin tone correction example (UOPB DB [4])

    16 different illumination + camera settings

    Tone correction

    Preserve melanin


    Result of tone correction1

    Result of Tone Correction

    • Skin tone correction example (UOPB DB [4])


    Conclusion

    Conclusion

    • Skin colour modelling:

      • Melanin and Hemoglobin concentration

      • Linear model in logarithm space

      • Estimation by Independent Component Analysis

    • Skin appearance + Light modelling:

      • Estimates light source

      • Preserves skin colour by melanin value

    • Applied to digital images from CCD cameras


    Reference

    Reference

    • [1] Shi, L., and Funt, B., "Skin Colour Imaging That Is Insensitive to Lighting," Proc. AIC (Association Internationale de la Couleur) Conference on Colour Effects & Affects, Stockholm, June 2008

    • [2] Angelopoulou, E., Molana, R., and Daniilidis, K. “Multispectral skin color modeling,” In IEEE Conf. on Computer Vision and Pattern Recognition, volume 2, pages 635-642, Kauai, Hawaii, Dec. 2001.

    • [3] Shimizu, H., Uetsuki, K., Tsumura, N., and Miyake, Y. Analyzing the effect of cosmetic essence by independent component analysis for skin color images. In 3rd Int. Conf. on Multispectral Color Science, pages 65-68, Joensuu, Finland, June 2001.

    • [4] Martinkauppi, B. “Face color under varying illumination-analysis and applications,” Ph.D. Dissertation, University of Oulu, 2002.


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