1. Face detection Tuck N.

2. IDEF0 decomposition

3. Face detection’s approaches Contour images analysis Intensity and colors analisys Adaptive classifier

4. Contour images analysis

5. Face detection’s approaches

6. Face detection’s approaches Skin color’s cluster

7. Adaptive classifier

8. Adaptive classifier

9. AdaBoost AdaBoost is an algorithm for constructing a “strong” classifier as linear combination f(x) =αtht(x) of “simple” “weak” classifiers ht(x) : X -> {−1,+1}.

10. Rapid Object Detection using a Boosted Cascade of Simple Features Integral Images

11. Algorithm workflow

12. Classifier’s algorithm workflow

13. OWL model We can use OWL model to store some data, used in our application. For example, I’ve stored maximum and minimum area of faces, that should be detected. According to w3c standarts: <owl:Restriction> <owl:onPropertyrdf:resource="#minWidth" /> <owl:minCardinalityrdf:datatype="&xsd;nonNegativeInteger">20</owl:minCardinality> </owl:Restriction> <owl:Restriction> <owl:onPropertyrdf:resource="#maxWidth" /> <owl:maxCardinalityrdf:datatype="&xsd;nonNegativeInteger">300</owl:maxCardinality> </owl:Restriction>

14. OWL and C# interaction We can use standartXmlDocumentReader to parse OWL model. Also we can use LINQ to parse this model.