1. A Reliable Skin Detection Using Dempster-Shafer Theory of Evidence
Mohammad Shoyaib, Mohammad Abdullah-Al-Wadud and Oksam Chae
Image Processing Lab
Department of Computer Engineering
Kyung Hee University

2. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence
Organization of the Presentation Ⅰ
Motivation and Objective Ⅱ
Available Approaches Ⅲ
Proposed System Ⅳ
Results Ⅴ
Conclusion

3. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence3
Motivation and Objective
Applications Depends on Skin detection
Face and person detection
Gesture recognition
Filtering (e.g., pornographic) web content
Video surveillance applications etc.
Challenges
Due to several Imaging condition (ethnicity, hairstyle, makeup, illumination, camera characteristics etc.) skin detection becomes challenging
Need Improvement
Can handle most of the imaging conditions
To support aforementioned applications detection should be performed in real time 3

4. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence4
Available Techniques
Explicit threshold based methods
These 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. 4

5. Proposed Method Selection of Color Space Find Source of Information
A Reliable Skin Detection Using Dempster Shafer Theory of Evidence
Proposed Method
Selection of Color Space
Find Source of Information
Take Final Dicision
R > 140
G > 75
B > 35
28 < (R – G) < 100
50 < (R – B) < 130
R > G and R > B
Convert the measures performance to mass valued
Fuse these mass value to take final decision.
Dempster Shafer Theory of Evidance
We use RGB color space
Six different Source of Information

6. Finding the Source of Information
A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 6
Finding the Source of Information
Figure: Distribution of Skin and non-skin clusters in R space
Figure: Distribution of Skin and non-skin clusters in G and B space. 6

7. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence7
Finding the Source of Information (contd..)
Figure: Plot of distribution of skin colors on different (RG and RB) planes 7

8. Finding the Source of Information (contd..)
A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 8
Finding the Source of Information (contd..)
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 - B.
(a) Distribution of skin and non-skin colors (b) Coverage of the selected criteria 8

9. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence9
Calculation of Mass Value
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. 9

10. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence10
Use of Dempster Shafer Theory of Evidence
Measures
Skin mass
Non-Skin mass
R > 140
G > 75
B > 35
28 < (R – G) < 100
50 < (R – B) < 130
R > G and R > B 10

11. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence11
Experimental Results
Performance comparison in terms of detection rates
Method
CDR (%)
FDR (%)
CR (%)
Bayesian Classifier
84.601
MoG Classifier
Proposed Method 11

12. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence12
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. 12

13. A Reliable Skin Detection Using Dempster Shafer Theory of Evidence13
Conclusion
Experimental results demonstrated that the proposed method can achieve both the robustness and the stability in skin detection
running time will be same as that of the Bayesian classifier. 13

14. Questions or Comments