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

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

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 3 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

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 4 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

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

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

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

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

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 9 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

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 10 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 10

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 11 Experimental Results 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 11

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 12 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

A Reliable Skin Detection Using Dempster Shafer Theory of Evidence 13 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

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