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逄霖生中國文化大學 電機工程學系
Outline Introduction Statistical Detection Models
Acquisition of Human Face Images Skin Detection Ocular Region Detection
Experimental Results ROC Evaluation Conclusions Future Works
IntroductionTo Detect human face by using statistical
methodsThe given image is treated as an random
variable. The colors and other features of data is treated
as the outcomes of the given random variable.The prior and posterior information can be used
to handle statistical data.The uncertainty of information reveals the
variations of data.The ROC curve statistically evaluates the
detection results.
Statistical Detection Models• Bayes’ Filter for Skin Detection
• Entropy Model for Eye Detection
• ROC (Receiver Operating Characteristic) curve for statistical evaluation of skin detection results
Statistical Detection Models• Bayes Rule
• Entropy
• ROC curve (Receiver Operating Characteristic
curve)• Statistical Evaluation of Detection Results
N
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Raw Image Data
Eye & Eyebrow Detection(Entropy analysis)
Skin Detection
(Bayes filter)
Face Detection
Mouth Detection(Color ratio
analysis)
PerformanceEvaluation
(ROC curve)
Color Conversion
Color Space Conversion1. RGB Primary colors (tri-stimulus values of
colors)
2. YCbCr Luminance & Chrominance
3. Gray Level s ＝ T(r)
where “s” is an output image, “r” is an input image
B
G
R
C
C
Y
r
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[Left] Original Image, [Right] Pre-selected Skin AreaNote that the eye & eye brow, mouth are not part of skin
Skin Detection• Applying a Bayes Filter to an image
where p(x) and p(y) are pdfs of random variables x and y, p(x|y) is the posterior probabilityp(y|x) is the prior probability.
)(
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0 50 100 150 200 2500
0.05
0.1
0.15
0.2
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0.3
0.35
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Pro
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Color (x=[0..255])
Cb-SkinCr-SkinCb-NSkinCr-NSkin
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Skin DetectionBy using a Bayes filter and a thresholding
method, the skin detection result of an image is shown as follow:
Morphology
Morphology
Entropy• Entropy( 熵 )
p(Ii) is the probability for the outcome Ii
Measure the degrees of uncertainty for different outcomes from a given random event
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ROC Curve• ROC Curve (Receiver Operating Characteristic
curve)
• ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from the cost context or the class distribution.
• ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision or quality making.
• It is widely used in binary discrimination evaluation.
TPP True Positive Possibility =sensitivity
FNP False Negative Possibility
FPP False Positive Possibility =1-specificity TNP True Negative Possibility
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1ROC
TP
P(s
en
sitiv
ity)
FPP(1-specificity)
CrCb
0 50 100 150 200 2500
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4p(x|y)
Pro
b. (n
orm
aliz
ed)
Color (x=[0..255])
Cb-SkinCr-SkinCb-NSkinCr-NSkin
0 50 100 150 200 2500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1TPP(sensitivity) & FPP(1-specificity)
TP
P &
FP
P
Color (x=[0..255])
TPP(Cr)FPP(Cr)TPP(Cb)FPP(Cb)
ROC Curve
Non-SkinArea
indeterminate Area
SkinArea
Cr FPP(cr)<0.96 0.96 FPP(cr)≦ TPP(cr)>0.9
Cb FPP(cb)>0.02 0.02 FPP(cb) 0.08≦ ≦ TPP(cb)<0.025
ConclusionsStatistical methods are able to classify and
detect human characteristics.
Using the prior information can help us to recognize the posterior situation.
The uncertainty of analyzed data gives the location of the area of eye.
ROC curve can determine the content of experimental results.
Future Works Adapted with Environmental
Variations
Hardware Acceleration
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