Parametri modelling

1.4 Overview of Dierent Methods of Skin Detetion

1.4.1 Skin detetion methods using stati framework

1.4.1.2 Parametri modelling

the eet of unontrolled illuminationonditions. Firstly, the

Y

^{omponents are arranged in}

desending order. The minimum valueof thetop 5% values ofthe

Y

^{omponentistermedasa}

parameter

E

^{,and remainingvaluesinthetop 5%are setto255. Similarly,themaximumvalue}

among the bottom 5% values of the

Y

^{omponentis termed asa parameter}

B

^{, and remaining}

values inthebottom5% are setto0. Finally,the intermediate

Y

^omponentsare re-alulated as:

g(x, y) = 255 × ln f(x, y) − ln B

ln E − ln B

^(1.27)

where,

g(x, y)

^{isthewhitebalanedluminanevalueatloation}

(x, y)

^,and

f (x, y)

^{istheoriginal}

luminane valuebeforewhite balaning. The skinolourmodelisdened by asetof boundary

rulesinRGBspae. TheauthorsalsofoundthatskindistributioninYCgCbspaetakesirular

shape. Finally, a skin mask is obtained by ANDing two skin models derived from RGB and

YCbCr spaes. Detetionperformane isfurther improved by inorporating atexture analysis

into this skin model. Textural features are extrated using the GLCM. For a given gray-sale

image

I

^{of size}

n × m

^{, the GLCM is given by:}

T (i, j ) =

n

X

x=1 m

X

y=1







1, ifI(x, y) = i ∧ I(x + ∆ x , y + ∆ y ) = j 0, otherwise,

(1.28)

where,

(∆ x , ∆ y )

^{isthe oset between the pixels}

I(x, y)

^and

I (x + ∆ x , y + ∆ y )

^{. The omputa-}

tional omplexity in determiningthe GLCM depends on the number of grey levels

g

^{,and itis}

proportionalto

O(g ² )

However, reently published literatures show that the performane of expliit boundary

speiation-based methods are not better than the model-based approahes [8℄.

skinolourdistribution. Inthiswork,statistialtestsareprovidedtoshowasetheadvantageof

usingGMMoverSGMforskinolourdistributionmodelling. Greenspanetal.[77℄showed that

GMM-basedrepresentation ofskin pixeldistributionismore robust toenvironmentalhanges,

suhasolourspaehanges,highlightsandshadows. TheyalsousedtwoGaussianomponents

forGMM,and onerepresentsthedistributionofskinolourundernormallight,whiletheother

representsthe distributionof the morehighlightedregionsof the skin. Caetano etal.[78℄ used

twotoeightGaussianomponentsforpixeldistributionmodellingin

rg

^{olourspaeforpeople}

having dierent skin tones. Lee and Yoo [55℄ proposed anelliptial modelling-basedapproah

for skin detetion. The elliptial modelling is less omputationally omplex than the GMM

modelling. However, many trueskinpixelsmay berejetedif theellipse issmall. On the other

hand, if the ellipse is suiently large, many non-skin pixels may be deteted as skin pixels.

TheyusedsixGaussianomponentstoimplementtheGMM.Ontheotherhand,Thuetal.[79℄

used four Gaussian omponents. Use of multipleGaussian enables detetion of dierent parts

ofafaewhihare illuminateddierently. Jones andRehg [13℄used twoseparate GMMs,eah

having 16 Gaussian omponents for skin and non-skin pixel distribution. A skin probability

map (SPM) for animage is derived fromthe two models using Bayes theorem. The SPM is a

2D array ofsize equaltothe image. An elementof theSPMrepresents aposterioriprobability

of a pixelbeing skin atthat loation.

The performane of these simple parametri models is limited due to two major fators

a) apparent hange in skin appearane due to unontrolled illuminationonditions, and b)

the presene of skin-like olours in image bakground. To overome these problems, dierent

authors proposed dierent improvements over simple parametri models for skin detetion.

Phung et al. [38℄ proposed an adaptive sheme to selet the optimum threshold for the SPM

by assuming that a skin region to be oherent and homogeneous in texture. Segmentation

auray of skin regions an be further improved by inorporating texture analysis in the

parametri modelling framework. Texture features an be extrated by performing texture

analysis in various domains, suh as graysale [75,80℄, olour [81℄, or skin map [82℄. In order

toextrattexture features,dierentauthorsuseddierentfeature desriptors. Jiang etal.[83℄

proposed a new approah by inorporating texture and spae analysis in a standard SPM

framework. An initialskin mask is derived for an image by thresholding the SPMwith a low

threshold. Subsequently, textural features are extrated using Gabor wavelets. This gives a

Figure 1.7: A owhart showing (a)trainingand (b)detetion proessesproposedbyKawulok [9℄

textural map for the image. The texture map is thresholded based on an assumption that

bakground regionsare oarser than skinregions. This givesa texture maskor atexture lter

whih is later ombinedwith the initialskin mask to obtaina more aurate skin mask. This

redues false aeptane error signiantly. Finally, the watershed segmentation is employed

with a set of well-dened region markers to grow skin regions to redue false rejetion error.

H.-M. Sun [84℄ proposed a loal adapation sheme for the Bayesian lassier as proposed by

Jones and Rehg [13℄. They generatedaloalskinmodelfromaset of skinpixelssamplesfrom

the image. Finally,the loalmodelisombinedwith theglobal ortrainedmodelinaweighted

sumapproah. P.NgandC.M.Pun ombined2-DDaubehies wavelets-basedtextureanalysis

with a GMM-based olour model [85℄. The 2-D Daubehies wavelets are alulated by using

the sub-images whihare entered ateahof the pixelloations. Texture feature ateah pixel

loationisrepresentedbythewavelet energyvetor

v e

^{,whihisobtainedbyapplyingShannon}

entropy on the wavelet oeients vetor

v c

^{. The}

v e

^{for all the pixel loations are nally}

grouped intoaset of lustersusing k-Means lusteringalgorithm. Finally,some ofthe lusters

are marked as non-skin basedon their Shanonentropies and eliminated aordingly. Kawulok

et al. [9℄ used linear disriminative analysis (LDA) to derive disriminative features between

skin and non-skin regions. In this method, LDA projetion matrix is derived by using olour

andloaltexturefeaturesfromasetoflabelledimages. TheLDAprojetionmatrixdependson

trainingdata. Therefore, LDAgivesaprojetionmatrix whihensures best possibleinter-lass

disrimination.

Another approahfollowsanuse of spatialanalysisof skinregionsbyexploitingthe spatial

alignment of skin pixels and their relation with neighbourhood pixels [14,80,86,87℄. These

approahessigniantlyreduefalsepositivesindetetingtheskinregions. Ingeneral,allthese

spatialanalysis-basedmethodsarebased onastandard SPM. Ruiz-del-SolarandVershae [86℄

proposed a skin detetion method whih uses a ontrolled diusion. The ontrolled diusion

proess has two steps: a) extration of diusion seeds, and b) atual diusion proess. The

diusion seeds are extrated by thresholding the SPM with a high threshold. In the diusion

step, skinregions are grown fromthe seeds by inludingthe neighborhoodpixelswhih satisfy

a given diusion riteria. The riteria depends on two fators a) dierene between soure

and a test pixel in diusion domain, and b) SPM value at the test pixel loation. Therefore,

this method works well if skin regions have sharp boundaries. A leak in diusion may our

if there are smooth transitions between pixels from one region to another. In 2010, Kawulok

proposed an energy-based sheme for skin blob analysis [87℄. Pixels with high valued SPM

values are seleted asskin seeds. These seed regionsare subjeted to morphologialerosionto

further redue false aeptane. In this method, seed pixels are assumed to have a maximal

energy, whih is likely to be spread over an image. The amount of energy transferred to an

adjaentpixelfromasourepixeldependsontheskinprobabilityoftheadjaentpixel. Apixel

is exluded fromskin region if there is noenergy leftto be passed ontoit from a sourepixel.

In 2013,M. Kawulok [14℄ proposed apropagation-basedregiongrowing method,whihutilises

spatial relationship between the pixels. Kawulok's method is based on Dijkstra's minimum

path-ost algorithm [88℄. In Kawulok's method, eah pixel is onsidered as an independent

node and the imageis the orresponding graph. In this method,the optimum values of region

growing parameters are seleted manually.

Thereareanotherlassofapproahesofskinsegmentation,whihusesomepriorinformation

about the atual skin olour of a person present in an image. In general, human skin olour

does not show signiant variations over the body. So, a fae detetor an be used to detet

the fae and extrat a set of pixels beloning to faial region. The prior information obtained

from the faial pixels is then utilized to segment out other skin regions of the human body.

A global skin detetion model an be loally adapted aording to the distribution of faial

skin pixels. Fritsh et al. [89℄ used fae detetion to derive a loal skin model for skin region

traking. In 2008, Kawulok [19℄ proposed adynami skin modelby using pixel harateristis

of faial regions. The global pixel statistis are fused with the loal statsitis of faial skin

pixels. Yogarajahet al.[20℄ used a dynami thresholding-basedmethodfor skindetetion. In

Figure 1.8: Proposed framework by Tan et al. [10℄: eye detetor, 2-D histogram, Gaussian model,

and fusionstrategy.

this method, a dynami threshold is obtained from the harateristis of skin pixelsextrated

fromthefaialregions. Tanet al.[10℄proposedafusion-basedskindeteion methodusingfae

detetion. Forthis,asmoothedolourhistogramandaGaussianmodelofskinisfusedtogether.

Kawulok et al. [21℄ showed that the seletion of seeds points using faial pixels an improve

the detetionaurayinaregiongrowing-basedskindetetion method. Pixels extratedfrom

the fae provide a good estimate of olour distribution of skin regions even in the presene of

skin-likebakgrounds and/orpoorilluminationonditions.