Expliit boundary speiation - Skin detetion methods using stati framework

1.4 Overview of Dierent Methods of Skin Detetion

1.4.1 Skin detetion methods using stati framework

1.4.1.1 Expliit boundary speiation

Boundary speiation forskin olourdepends ona set of thresholds and onditions whih

ould beeither dened in thesame olour spae,(e.g. RGB) orina transformed olourspae,

suh asYCbCr, HSV, CIELab et.

One of the earliestmethodsofskin detetionis proposed by Sobottkaand Pitas [43℄. They

proposed a skin detetion boundary along

S

^and

H

^hannels ⁱⁿ ^HSV ^olour ^spae ^as

S ∈ [0.23, 0.68]

^and

H ∈ [0, 50]

^. ^Later, ^T^sekeridou ^and ^Pitas ^proposed ^a ^modiation ^[2℄ ^to ^this

methodforfaeregionsegmentationinanimagewatermarkingsystem[72℄. Theorresponding

boundary rule in the HSV olour spae isas follows:







(0 ≤ H 6 25) ∨ (335 6 H 6 360) (0 6 S 6 0.6) ∧ (0.4 6 V )

(1.21)

Figure 1.6-a shows the projetion of the above rules onto the RGB olour spae. Here, darker

shade shadeimplieshigher density ofskinpixels. Solina etal.[3℄proposed anotherset of xed

Figure 1.6: Skin olour distributions in dierent olour planes: (a) Tsekeridou and Pitas [2℄, (b)

Solina etal.[3℄, ()Hsuetal. [4℄,(d)Kukharev andNowosielski [5℄,(e) A.Cheddad etal.[6℄,and (f)

Y.-H.Chen etal.[7℄. (Note: thegure istaken from [8℄)

rules in RGB spae for fae detetion of people havingfair omplexionas:



 



 



(R > 95) ∧ (G > 40) ∧ (B > 20) max(R, G, B) − min(R, G, B) > 15

|R − G| 6 15 ∧ (R > G) ∧ (R > B)

inuniform daylight illumination (1.22)

or,







(R > 220) ∧ (G > 210) ∧ (B > 170)

|R − G| 6 15 ∧ (R > G) ∧ (R > B)

in ashlight lateralillumination (1.23)

For unknown lightingonditions, a pixelis lassiedas skin if itsatises one of the above two

onditions. These rules are illustrated inFigure 1.6-b in R-G, R-B, G-B and r-g planes. Hsu

et al. [4℄proposed a boundary rule based on YCbCr olour spae. The authors observed that

the shape of skin tone luster in Cb-Cr spae an be approximated as an elliptial struture

wherethe lusterloationdepends onluminane

Y

^. ^They ^performed ^a^non-linear^modiation

C b

^and

C r

^values ^if

Y < 125

^or

Y > 188

^. Subsequently, the skin pixel luster is modelled as an ellipse in a transformed spae

Cb ^′ Cr ^′

^. ^The ^equiv^alent ^results ^for ^skin distribution in RGB spae is shown in Figure 1.6-. Kukharev and Nowosielski proposed another set of skin

detetion rules [5℄ using RGB and YCbCr olour spaes asfollows:



 



 



(R > G) ∧ (R > B)

{(G > B) ∧ (5R − 12G + 7B > 0)} ∨ {(G < B) ∧ (5R + 7G − 12B > 0)}

{Cr ∈ (135, 180)} ∧ {Cb ∈ (85, 135)} ∧ (Y > 80)

(1.24)

TheorrespondingmodelrepresentationinRGBandrgspaeisgiveninFigure1.6-d. Cheddad

et al. [6℄transformed the normalized RGB olour spae intoa single-dimensional error signal,

where the skin olour distribution an be modelled as a Gaussian urve [6℄. Subsequently,

a pixel is lassied as a skin if its 1D equivalent value lies within the two threshold values

determined by the standard deviation of the urve. The skin model is shown in Figure 1.6-e.

Reently,Chenetal. proposedanewRGBsubspae forskindetetionbysubtratingtheRGB

values:

sR = R − G

sG = G − B

sB = R − B

^. Subsequently, they proposed aboundary rule

{(−142 < sR < 18) ∧ (−48 < sG < 92) ∧ (−32 < sB < 192)}

^. ^The ^rules ^are illustrated in Figure 1.6-f. In [73℄, Shaik et al. ompared HSVand YCbCr spaes for skindetetion using a

Table 1.1: Mostpopular examplesof olour spaes usedin skindetetion: RGB, YCbCr, HSV and

advanedspaeER/GH [18℄

Colour

spae

Range of omponents Restritions for skin olour

RGB R,G,B: [0,255℄

R > 95 ∧ G > 40 ∧ B > 20 ∧

{max (R, G, B) − min (R, G, B) > 15} ∧ |R − G| >

15 ∧ R > G ∧ R > B

YCbCr Y, Cb,Cr: [0, 255℄

Y > 80 ∧ 77 < Cb < 127 ∧ 133 < Cr < 173

HSV H: [

0 ^◦

360 ^◦

^℄,^S,^V: ^[0,^1℄

0 ^◦ < H < 50 ^◦ ∧ 0.1 < S < 0.68 ∧ 0.35 < V < 1

ER/GH R,G: [0, 255℄, H:[

0 ^◦

360 ^◦

^℄

13.4224 < E ∧ R/G < 1.7602 ∧ H < 23.89

boundary-based method. In 2015,Sawiki and Miziolek[18℄ proposed anotherset of boundary

rules in CMYK spae asfollows:

•

^Before ^ROC ^analysis:

(K < 205) ∧ (0 6 C 6 0.05) ∧ (0.089 < Y < 1) ∧ (0 6 C/Y < 1) ∧ (0.1 6 Y /M < 4.8)

(1.25)

•

^After ^ROC ^analysis:

(K < 205) ∧ (0 6 C 6 0.05) ∧ (0.0909 < Y < 0.945) ∧ (0.1 6 Y /M < 4.67)

^(1.26)

Apart from these simple boundary speiations in dierent olour spaes, advaned ap-

proahes are also proposed for a more aurate 3D desription of skin luster. For example,

Garia and Tziritas [74℄ proposed a skin detetion method by utilizing a set of planes in the

YCbCr spae. Brandand Mason [50℄ performed a omparativeanalysis of algorithmsin three

olour spaes: RGB, YES and YIQ. In their analysis, parametri thresholds and statistial

funtions are used. Thresholding of the

R/G

^ratios îs âlso ^performed. În ^[51℄, â ^new ôlour

spae

ER/GH

îs ^proposed ^by ^mixingôf ôlour ômponents. În ^the ^pseudo^spae

ER/GH

E

belongs to YES,the

R/G

^ratio ^is ^from^RGB ^spae, ^and

H

^is^from ^HSV.

Some of the authors used additional information like texture features to improve skin de-

tetion. For example, Wang et al. [75℄ used gray-level o-ourrene matrix (GLCM) for skin

detetion. In this method,a whitebalaning is performed in YCbCr olourspae to minimize

the eet of unontrolled illuminationonditions. Firstly, the

Y

ômponents âre ârranged ⁱⁿ

desending order. The minimum valueof thetop 5% values ofthe

Y

ômponentîs^termedâsâ

parameter

E

^,^and ^remaining^valuesⁱⁿ^the^top ^5%^are ^set^to^255. ^Similarly,^the^maximum^value

among the bottom 5% values of the

Y

ômponentîs ^termed âsâ ^parameter

B

^, ^and ^remaining

values inthebottom5% are setto0. Finally,the intermediate

Y

^omponents^are re-alulated as:

g(x, y) = 255 × ln f(x, y) − ln B

ln E − ln B

^(1.27)

where,

g(x, y)

^is^the^white^balaned^luminane^value^at^loation

(x, y)

^,^and

f (x, y)

^is^the^original

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 )

^is^the ^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

^,ând îtîs

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℄.

Dalam dokumen Novel framework for segmentation of skin regions using chromatic and textural information (Halaman 44-48)

Expliit boundary speiation

1.4 Overview of Dierent Methods of Skin Detetion

1.4.1 Skin detetion methods using stati framework

1.4.1.1 Expliit boundary speiation

S

H

S ∈ [0.23, 0.68]

H ∈ [0, 50]







(0 ≤ H 6 25) ∨ (335 6 H 6 360) (0 6 S 6 0.6) ∧ (0.4 6 V )



 

 



 

 



(R > 95) ∧ (G > 40) ∧ (B > 20) max(R, G, B) − min(R, G, B) > 15

|R − G| 6 15 ∧ (R > G) ∧ (R > B)







(R > 220) ∧ (G > 210) ∧ (B > 170)

|R − G| 6 15 ∧ (R > G) ∧ (R > B)

Y

C b

C r

Y < 125

Y > 188

Cb ′ Cr ′



 

 



 

 



(R > G) ∧ (R > B)

{(G > B) ∧ (5R − 12G + 7B > 0)} ∨ {(G < B) ∧ (5R + 7G − 12B > 0)}

{Cr ∈ (135, 180)} ∧ {Cb ∈ (85, 135)} ∧ (Y > 80)

sR = R − G

sG = G − B

sB = R − B

{(−142 < sR < 18) ∧ (−48 < sG < 92) ∧ (−32 < sB < 192)}

R > 95 ∧ G > 40 ∧ B > 20 ∧

{max (R, G, B) − min (R, G, B) > 15} ∧ |R − G| >

15 ∧ R > G ∧ R > B

Y > 80 ∧ 77 < Cb < 127 ∧ 133 < Cr < 173

0 ◦

360 ◦

0 ◦ < H < 50 ◦ ∧ 0.1 < S < 0.68 ∧ 0.35 < V < 1

0 ◦

360 ◦

13.4224 < E ∧ R/G < 1.7602 ∧ H < 23.89

•

(K < 205) ∧ (0 6 C 6 0.05) ∧ (0.089 < Y < 1) ∧ (0 6 C/Y < 1) ∧ (0.1 6 Y /M < 4.8)

•

(K < 205) ∧ (0 6 C 6 0.05) ∧ (0.0909 < Y < 0.945) ∧ (0.1 6 Y /M < 4.67)

R/G

ER/GH

ER/GH

E

R/G

H

Y

Y

E

Y

B

Y

g(x, y) = 255 × ln f(x, y) − ln B

ln E − ln B

g(x, y)

(x, y)

f (x, y)

I

n × m

T (i, j ) =

Cb ^′ Cr ^′

0 ^◦

360 ^◦

0 ^◦ < H < 50 ^◦ ∧ 0.1 < S < 0.68 ∧ 0.35 < V < 1

0 ^◦

360 ^◦

O(g ² )