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Estimation of Skin Color Range Using Achromatic Features

Wen-Hung Liao Department of Computer Science National Chengchi University November 27, 2008. Estimation of Skin Color Range Using Achromatic Features. Outline. Motivation and Related Work Color Spaces Fixed vs. Dynamic Range Approach Experimental Results Skin color segmentation

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Estimation of Skin Color Range Using Achromatic Features

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  1. Wen-Hung Liao Department of Computer Science National Chengchi University November27, 2008 Estimation of Skin Color Range Using Achromatic Features

  2. Outline • Motivation and Related Work • Color Spaces • Fixed vs. Dynamic Range Approach • Experimental Results • Skin color segmentation • Hand & finger detection • Conclusion

  3. Background • Previous claims: skin color is restricted to a “fixed” range in certain color coordinates: • Sobottka& Pitas: Hue:[0,50º], Saturation:[0.23,0.68] • Chai& Ngan: Cb:[77,127], Cr[137,177] • Kawato& Ohya: Decision boundary in normalized RGB space

  4. Decision Boundary in Normalized RGB Space

  5. Sobottka& Pitas: Fixed Hue + Saturation

  6. Chai& Ngan: Fixed Cb,Cr

  7. Kawato& Ohya

  8. Comparative Analysis From: Phung et al, Skin segmentation using color pixel classification: analysis and comparison, IEEE Transactions on PAMI, 2005.

  9. Observation • It is true that the skin color lies in a small range, yet this range tends to shift under different lighting conditions. • Question: Is it possible to dynamically adjust the range of skin color to enhance the robustness of color-based segmentation?

  10. The Proposed Solution • Use achromatic information (face detection) to help determine the range. • Limitation: • Face must be present and detected. • Suitable for vision-based human computer interface.

  11. Five Classes of Color Space

  12. Color Spaces Investigated * Dynamically set the threshold in Hue domain

  13. Determining the Threshold (I) W0 (X0, Y0) H0 • Step 1: detecting and locating the face • Step 2: mark the cheek area X = X0 +(W0 /5) Y = Y0 +(H0 /2) width = W0 /5 height = H0 /5 • Step 3: obtain the hue distribution of the marked area.

  14. Determining the Threshold (II) 0 255 • Step 4: assume that the histogram is peaked at A: • search to the left and right of A until • Local minimum <A/10 is uncovered • A non-zero global minimum is found

  15. Face Detection using DSE • Directional Sobel Edges

  16. Experiment: Skin Color Segmentation • Compare the performance of 5 different methods: • Dynamic threshold • Fixed threshold– fixed Hue • Kawato& Ohya – fixed Normalized RGB • Sobottka& Pitas – fixed Hue & Saturation • Chai& Ngan – fixed Cb & Cr • Material • Images captured by a low-cost webcam under different lighting conditions. • A total of 400 images (taken indoor) are manually segmented and labeled.

  17. Skin Color Segmentation: Experimental Results

  18. Best and Worst Case Performance

  19. Recall and Precision Recall = TP/(TP+FP) Precision = TP/(TP+FN)

  20. Speed-up the Process 1. Detecting Face (After K frames) 2. Record color distribution of cheek area 3. Tracking face 4. Local search 5. Update color distribution

  21. Performance Improvement

  22. Experiment: Hand Detection • Color-based hand segmentation • No post-processing • Does not involve statistical modeling and classifier

  23. Plamar vs. Dorsal Side Hue histogram Hue histogram

  24. Hand Detection: Experimental Results

  25. Fingertip Detection 150 images

  26. Conclusion Perform comparative evaluation of several color-based segmentation methods. Propose and implement a dynamic range estimation algorithm using achromatic features. Superior performance in terms of skin-color segmentation, hand and finger detection. Suitable for vision-based HCI.

  27. Thank you Q & A

  28. Experimental Result worst TP Dynamic Threshold

  29. Experimental Result worst TP Fixed Hue

  30. Experimental Result worst TP Fixed Normalized RGB

  31. Experiment Result worst TP Fixed Hue & Saturation

  32. Experiment Result worst TP Fixed Cb & Cr

  33. Recall = TP/(TP+FP) Precision = TP/(TP+FN)

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