Simon Fraser UniversityComputational Vision Lab

Lilong Shi, Brian Funt and Tim Lee

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

Tone correctio

n

Preserve melanin

Skin tone correction

Melanin/Hemoglobin separation

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

Two-layered Skin Model [2] Epidermis Layer: melanin absorbance Dermis Layer: hemoglobin absorbance

A layer has properties of an optical filter

Various skin colour <= melanin +

hemoglobin Genetic: Race Temporary:

▪ Exposure to UV ▪ Hot bath

Mixture varying by 2 independent factors Analyse melanin and hemoglobin factors

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

Original Source Signals Observed SignalsMixing

s1

s2

70%

30%

v1

s × A = v

20%

80%

v20%

100%

v3

Melanin

Hemoglobin

Skin samples

Melanin

Hemoglobin

Typical skin spectrum Visible wavelength 400nm – 700nm

Extract skin bases from observed spectrum by ICA

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.

Arbitrary skin spectrum can be approximated cσσ hhmm

constru

are variableshm ,

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

cσσpixel hhmm

Possible skin colours lie within plane

Given a pixel from

skin, compute

by projecting

log(R,G,B) onto

hm ,

hm σσ ,

Input Image [3]

Melanin Image

Hemoglobin Image

m

h

cσσ hhmm

- Inverse melanin concentration- Inverse hemoglobin concentration

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

Colour: illumination times reflectance

In log space, multiplication => addition:

cωσσΠ τhhmmhm ),,(

Illum. basis

In practice

Drop hemoglobin basis ▪ Small angle between Illum and hemoglobin axes

Ignore brightness Skin colour varying by T and

( , )m m m τ Π σ ω

m

384 real skin reflectances times 67 real light sources

=> 25728 samples

( , )m m m τ Π σ ω

Skin tone correction example (UOPB DB [4])

20

Tone correction

Preserve melanin

16 different illumination +

camera settings

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

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

[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.