mohammad shoyaib, mohammad abdullah-al-wadud and oksam chae image processing lab department of...

Mohammad Shoyaib, Mohammad Abdullah-Al-Wadud and Oksam Chae

Image Processing Lab

Department of Computer Engineering

Kyung Hee University

A Reliable Skin Detection Using Dempster-Shafer Theory of Evidence

22

ⅠⅠ

ⅡⅡ

ⅢⅢ

Motivation and Objective

Proposed System

Available Approaches

Organization of the Presentation

Results

ConclusionⅤⅤ

A Reliable Skin Detection Using Dempster Shafer Theory A Reliable Skin Detection Using Dempster Shafer Theory of Evidenceof Evidence

ⅣⅣ

33

Motivation and Objective


Applications Depends on Skin detection Face and person detection Gesture recognition Filtering (e.g., pornographic) web content Video surveillance applications etc.

Applications Depends on Skin detection Face and person detection Gesture recognition Filtering (e.g., pornographic) web content Video surveillance applications etc.

Need Improvement

-Can handle most of the imaging conditions

-To support aforementioned applications detection should be performed in real time

Need Improvement

-Can handle most of the imaging conditions

-To support aforementioned applications detection should be performed in real time

Challenges

-Due to several Imaging condition (ethnicity, hairstyle, makeup, illumination, camera characteristics etc.) skin detection becomes challenging

44

Available Techniques

Explicit threshold based methodsThese methods explicitly define the boundaries of the skin cluster in certain color spaces using a set of fixed thresholds

. Parametric methods Single Gaussian, Mixture of Gaussian etc.

Parametric methods Bayesian classifier, self organizing map (SOM), normalized lookup table (LTU) etc are the key ideas in this group.


55

Selection of Color Space

Selection of Color Space

Take Final DicisionTake Final DicisionFind Source of

InformationFind Source of

Information

R > 140G > 75B > 35

28 < (R – G) < 10050 < (R – B) < 130R > G and R > B

Convert the measures performance to mass valued

Fuse these mass value to take final decision.

We use RGB color space

Six different Source of Information

Dempster Shafer Theory of Evidance

Proposed Method

050

100150

200250

0

100

200

3000

50

100

150

200

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300

G

BR


66

Finding the Source of Information

0 50 100 150 200 250 3000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

6

R

Fre

quen

cy o

f R

Skin

Non-Skin

0 50 100 150 200 250 3000

2

4

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16x 10

5

G

Fre

quen

cy o

f G

Skin

Non-Skin

0 50 100 150 200 250 3000

0.5

1

1.5

2

2.5x 10

6

B

Fre

quen

cy o

f B

Skin

Non-Skin

Figure: Distribution of Skin and non-skin clusters in R space

Figure: Distribution of Skin and non-skin clusters in G and B space. Figure: Distribution of Skin and non-skin clusters in G and B space.


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Finding the Source of Information (contd..)


0 50 100 150 200 250 3000

50

100

150

200

250

300

R

G

R > 140

G > 75

0 50 100 150 200 250 3000

50

100

150

200

250

300

R

B

R>140

B>35

Figure: Plot of distribution of skin colors on different (RG and RB) planes

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Finding the Source of Information (contd..)


0 50 100 150 200 250 3000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

6

R - G

Fre

quen

cy o

f (R

- G

)

Skin

Non-Skin

0 50 100 150 200 250 3000

50

100

150

200

250

300

R

G

28<(R-G) < 100

Figure: Clustering based on R – G.(a) Distribution of skin and non-skin colors (b) Coverage of the selected criteria

Figure: Clustering based on R – G.(a) Distribution of skin and non-skin colors (b) Coverage of the selected criteria

0 50 100 150 200 250 3000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

6

R - B

Fre

quen

cy o

f (R

- B

)

Skin

Non-Skin

0 50 100 150 200 250 3000

50

100

150

200

250

300

R

B

50 < (R – B) <130

Figure: Clustering based on R - B.(a) Distribution of skin and non-skin colors (b) Coverage of the selected criteria

Figure: Clustering based on R - B.(a) Distribution of skin and non-skin colors (b) Coverage of the selected criteria

99

Calculation of Mass Value


Si

Si

Si

SiS

i FPTP

FPTPADR

NSi

NSi

NSi

NSiNS

i FPTP

FPTPADR

ADRiS = Absolute Detection Rate for skin

TPiS = Total number of skin pixels correctly classified as skin.

FPiS = Total number of non-skin pixels incorrectly classified as skin.

ADRiNS = Absolute Detection Rate for Nonskin

TPiNS = Total number of non-skin pixels correctly classified as non-skin.

FPiNS = Total number of skin pixels incorrectly classified as non-skin.

ADRiS = Absolute Detection Rate for skin

TPiS = Total number of skin pixels correctly classified as skin.

FPiS = Total number of non-skin pixels incorrectly classified as skin.

ADRiNS = Absolute Detection Rate for Nonskin

TPiNS = Total number of non-skin pixels correctly classified as non-skin.

FPiNS = Total number of skin pixels incorrectly classified as non-skin.

1010

Use of Dempster Shafer Theory of Evidence


Measures Skin mass Non-Skin mass

R > 140 0.364339536 0.52453334

G > 75 0.147315460 0.43009921

B > 35 0.079768560 0.42793030

28 < (R – G) < 100

0.547064000 0.64447500

50 < (R – B) < 130

0.755432840 0.54223200

R > G and R > B 0.387888000 0.96809000

R G and R B ({ }) 0.387888m Skin 0})({BR andG R NonSkinm

R G and R B ({ , }) 1 0.387888 0.612112m Skin NonSkin

( ) 0(1)( ) 1

S

m

m S

1

1

... 1

... 1

( )

( )(2)1

( ).

n

n

n

i iS S S i

n

i iS S i

m S

m SK

K m S

1111

Experimental Results

Performance comparison in terms of detection rates

Performance comparison in terms of detection rates

Method CDR (%) FDR (%) CR (%)

Bayesian Classifier 84.601 27.00313 74.41969

MoG Classifier 98.38065 39.78734 64.89256

Proposed Method 90.24991 18.04092 82.97565


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Experimental Results (contd..)


Figure Results different skin detection methods (a) Original Image (b) Detection by Bayesian classifier (c) Detection by MoG classifier

(d) Detection by the proposed approach.

1313

Conclusion

Experimental results demonstrated that the proposed method can achieve both the robustness and the stability in skin detectionrunning time will be same as that of the Bayesian classifier.


Questions or Comments

mohammad shoyaib, mohammad abdullah-al-wadud and oksam chae image processing lab department of...

Documents

distribution of skin

skin detectionface

nonskin clusters

nonskin colors b coverage

total number of skin

facial images

absolute detection rate

r bconvert