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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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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