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1
Skin Color Detection through Estimation and Conversion
of Illuminant Color Under Various Illuminations
IEEE Transactions on Consumer Electronics
Authors: Hyun-Chul Do, Ju-Yeon You, and Sung-Il Chien
Speaker:吳昱慧Date:2010/03/23
2
Outline
• Introduction
• Skin color detection through estimation and conversion of illuminant color
• Experimental results and discussion
• Conclusion
3
Introduction
• Size invariant and facilitates
• Skin color in image applications– Image content filtering– Content-aware video compression
• HSV color space is closer to the humen perception
4
Introduction
• Uses a static model in image under various illumination conditions
• Robust skin color detection considering the illumination conditions
• Can then be detected using the skin locus and adaptive histogram backprojection
• For more effective estimation of the illuminant color
5
Skin color detection through estimation and conversion of illuminant color
Gamma corrected input image
Extraction of eye region candidates
Inverse gamma correction
Selection of the best pair of eyes
Extraction of sclera of the eye
Estimation of illuminant color
Canonical illuminant
Eye region template
Knowledge based rules
RiGiBi to XiYiZi
conversion
XcYcZc to RcGcBc
conversion
Gamma correction
Skin color detection using HSV color
Space
Skin color detected image
Conversion matrix calculation
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Skin color detection through estimation and conversion of illuminant color
A. Illuminant color estimation using sclera
• The eye detection is performed for segmenting the sclera region in an image
• Traditional eye dection methods can be classified into three categories
– Template-based methods– Appearance-based methods– Feature-based methods
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Skin color detection through estimation and conversion of illuminant color
• Eye detection in a complex image is very difficult
• Eye detection can be simply and efficiently implemented for frontal face images
• Edge-based template matching and knowledge-based rules
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Skin color detection through estimation and conversion of illuminant color
• Pupil has a low intensity the sclera has a high intensity
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Skin color detection through estimation and conversion of illuminant color
• Candidates are selected using the eye template and the edge of the input image
• Is carried out in the frequency domain via FFT
2
),(
),(
1
),(1
),(1
)/()(tan
sin
i
i
i
Ayxi
ii
Ayxii
jjiij
jijiijij
EyxYA
yxYA
E
xxyywhere
EED
10
Skin color detection through estimation and conversion of illuminant color
• Finally estimated by averaging the values within the segmented sclera region
BGR
11
Skin color detection through estimation and conversion of illuminant color
B. Skin color detection through illuminant color conversion
yxzZYX
Yy
ZYX
Xx
1,,
12
Skin color detection through estimation and conversion of illuminant color
• Assumed that the estimated and target luminance values remain the same that is﹐ ﹐
1e
tY
Y
13
Skin color detection through estimation and conversion of illuminant color
• Hue(H) ﹕spectral composition • Saturation(S) ﹕refer to the purity
• Value(V) ﹕brightness
14
Experimental results and discussion
• Four types of illumination– Horizon sunlight (incandescent, 2300K; marked
“H”)– Incandescent A (CIE A, 2856K; marked “A”)– Fluorescent TL84 (4000K; marked “T”)– Daylight source (CIE D65, 6500K; marked “D”)
15
Experimental results and discussion
16
Experimental results and discussion
17
Experimental results and discussion
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Conclusion
• Estimated using the pixels of the sclera region that are segmented from the eyes in face images
• Face detection and skin color enhancement even under changing illumination