1. By Using Statistical Models to Detect the Characteristics of Human Face 利用統計模型在彩色圖像 中偵測人臉特徵
逄霖生
中國文化大學 電機工程學系

2. Outline Introduction Statistical Detection Models Experimental Results
Acquisition of Human Face Images
Skin Detection
Ocular Region Detection
Experimental Results
ROC Evaluation
Conclusions
Future Works

3. Introduction To Detect human face by using statistical methods
The 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.

4. 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

5. Statistical Detection Models
Bayes Rule
Entropy
ROC curve (Receiver Operating Characteristic curve)
Statistical Evaluation of Detection Results

6. Flow Chart Raw Image Data Skin Detection (Bayes filter)
Color Conversion
Eye & Eyebrow Detection
(Entropy analysis)
Mouth Detection
(Color ratio analysis)
Performance
Evaluation
(ROC curve)
Face Detection

7. Color Space Conversion
RGB Primary colors (tri-stimulus values of colors)
YCbCr Luminance & Chrominance
Gray Level s ＝ T(r)
where “s” is an output image, “r” is an input image

8. Image Acquisition
[Left] Original Image, [Right] Pre-selected Skin Area
Note that the eye & eye brow, mouth are not part of skin

9. 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 probability
p(y|x) is the prior probability.

10. Skin Detection

11. Skin Detection
By using a Bayes filter and a thresholding method, the skin detection result of an image is shown as follow:

12. Skin Detection
Morphology

13. 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

14. Ocular Region Detection

15. Lip/Mouth Detection

16. Experimental Result

17. 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.

18. Evaluation of Skin Detection
TPP True Positive Possibility =sensitivity
FNP False Negative Possibility
FPP False Positive Possibility =1-specificity
TNP True Negative Possibility

20. Non-Skin Area indeterminate Skin
ROC Curve
Non-Skin
Area
indeterminate
Skin 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

21. Conclusions
Statistical 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.

22. Future Works Adapted with Environmental Variations
Hardware Acceleration