Post on 13-Jan-2016
description
Wen-Hung Liao
Department of Computer ScienceNational Chengchi University
November 27, 2008
Estimation of Skin Color Range Using Achromatic
Features
Outline
Motivation and Related WorkColor SpacesFixed vs. Dynamic Range ApproachExperimental Results
Skin color segmentationHand & 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
Decision Boundary in Normalized RGB Space
Sobottka & Pitas: Fixed Hue + Saturation
Chai & Ngan: Fixed Cb,Cr
Kawato & Ohya
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 space Representative 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 spaces Mixture
Color Spaces Investigated
color space domains
RGB Red 、 Green 、 Blue
HSV Hue 、 Saturation 、 Value
CIELab L、 a、 b
YCbCr Y、 Cb 、 Cr
CIELuv L、 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 = Y0 +(H0 /2) width = W0 /5 height = H0 /5
Step 3: obtain the hue distribution of the marked area.
(X(X00, Y, Y00))WW00
HH00
Determining the Threshold (II)
Step 4: assume that the histogram is peaked at A: search to the left and right of A
untilLocal minimum <A/10 is
uncoveredA non-zero global minimum is found
0 255
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.
Skin Color Segmentation: Experimental Results
false positive
false negative
true negative
true positive
Dynamic Threshold
0.0736 0.1706 0.9264 0.8294
fixed Hue 0.2125 0.3361 0.7875 0.6639
fixed Normalized RGB
0.0504 0.5303 0.9496 0.4697
fixed Hue & Sat
0.0588 0.5747 0.9412 0.4253
fixed Cr & Cb 0.0857 0.2996 0.9143 0.7004
Best and Worst Case Performance
best TP worst TP
Dynamic Threshold
0.9947 0.3494
fixed Hue 0.9977 0.0733
fixed Normalized RGB
0.9055 0.0002
fixed Hue & Sat 0.8891 0.0005
fixed Cr & Cb 0.9447 0.2234
Recall and Precision
00.10.20.30.40.50.60.70.80.9
1
adaptive fixed Hue fixed RGB fixed Hue& Sat
fixed Cr &Cb
Recall Precision
Recall = TP/(TP+FP)Precision =
TP/(TP+FN)
Speed-up the Process1. Detecting Face
2. Record color distribution of cheek area
3. Tracking face 4. Local search
5. Update color distribution
(After K frames)
Performance Improvement
0
5
10
15
20
25
30
0 10 20 30 40
K
FPS
Experiment: Hand Detection
Color-based hand segmentation No post-processing Does not involve statistical modeling
and classifier
Plamar vs. Dorsal Side
Hue histogram
Hue histogram
Hand Detection: Experimental Results
Hand detection
Dorsal sideDorsal side
(fingers)Plamar side
Plamar side (fingers)
Accuracy 92.65% 94.26% 90.78% 95.01%
Fingertip Detection
150 images# of
fingers detected
Dynamic threshold Fixed Threshold
5 108 72% 17 11%
4 21 14% 22 15%
3 10 7% 23 15%
2 5 3% 20 13%
1 1 1% 20 13%
0 5 3% 48 33%
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.
Q & A
Thank you
Experimental Result
Dynamic Threshold worst TP
Experimental Result
Fixed Hue worst TP
Experimental Result
Fixed Normalized RGB worst TP
Experiment Result
Fixed Hue & Saturation worst TP
Experiment Result
Fixed Cb & Cr worst TP
Recall = TP/(TP+FP)Precision =
TP/(TP+FN)