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:
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 Hand & finger detection Conclusion
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
Comparative Analysis From: Phung et al, Skin segmentation using color pixel classification: analysis and comparison, IEEE Transactions on PAMI, 2005.
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?
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.
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
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
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
Face Detection using DSE Directional Sobel Edges
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.
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.