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Wen-Hung Liao Department of Computer Science National Chengchi University November 27, 2008 Estimation of Skin Color Range Using Achromatic Features.

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Presentation on theme: "Wen-Hung Liao Department of Computer Science National Chengchi University November 27, 2008 Estimation of Skin Color Range Using Achromatic Features."— Presentation transcript:

1 Wen-Hung Liao Department of Computer Science National Chengchi University November 27, 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 Color spaceRepresentative color space Basic color spaces RGB 、 normalized RGB Perceptual color spaces HSV 、 HIS Orthogonal color spaces YCbCr 、 YUV Perceptually uniform color spaces CIELab 、 CIELuv Other color spacesMixture

12 Color Spaces Investigated color spacedomains RGB Red 、 Green 、 Blue HSV Hue 、 Saturation 、 Value CIELab L、a、bL、a、b YCbCr Y 、 Cb 、 Cr CIELuv L、u、vL、u、v * Dynamically set the threshold in Hue domain

13 Determining the Threshold (I) Step 1: detecting and locating the face Step 2: mark the cheek area X = X0 +(W0 /5) Y = Y 0 +(H 0 /2) width = W 0 /5 height = H 0 /5 Step 3: obtain the hue distribution of the marked area. (X 0, Y 0 ) W0W0W0W0 H0H0H0H0

14 Determining the Threshold (II) Step 4: assume that the histogram is peaked at A: search to the left and right of A until Local minimum { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/4087012/13/slides/slide_13.jpg", "name": "Determining the Threshold (II) Step 4: assume that the histogram is peaked at A: search to the left and right of A until Local minimum

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 false positive false negative true negative true positive Dynamic Threshold 0.07360.17060.92640.8294 fixed Hue0.21250.33610.78750.6639 fixed Normalized RGB 0.05040.53030.94960.4697 fixed Hue & Sat0.05880.57470.94120.4253 fixed Cr & Cb0.08570.29960.91430.7004

18 Best and Worst Case Performance best TPworst TP Dynamic Threshold 0.99470.3494 fixed Hue0.99770.0733 fixed Normalized RGB 0.90550.0002 fixed Hue & Sat0.88910.0005 fixed Cr & Cb0.94470.2234

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

20 Speed-up the Process 1. Detecting Face 2. Record color distribution of cheek area 3. Tracking face4. Local search 5. Update color distribution (After K frames)

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

24 Hand Detection: Experimental Results Hand detection Dorsal side Dorsal side (fingers) Plamar side Plamar side (fingers) Accuracy92.65%94.26%90.78%95.01%

25 Fingertip Detection 150 images # of fingers detected Dynamic thresholdFixed Threshold 510872%1711% 42114%2215% 3107%2315% 253%2013% 111%2013% 053%4833%

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 Q & A Thank you

28 Experimental Result Dynamic Threshold worst TP

29 Experimental Result Fixed Hue worst TP

30 Experimental Result Fixed Normalized RGB worst TP

31 Experiment Result Fixed Hue & Saturation worst TP

32 Experiment Result Fixed Cb & Cr worst TP

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


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