The document discusses different approaches to face detection including contour image analysis, intensity and color analysis, and adaptive classifiers. It specifically mentions AdaBoost as an algorithm that constructs a strong classifier as a linear combination of weak classifiers. It also discusses using OWL models to store data like maximum and minimum face areas to be detected, and how to parse OWL models using standard XML readers or LINQ in C#.

1. Face detectionTuck N.

2. IDEF0 decomposition

3. Face detection’s approachesContour images analysisIntensity and colors analisysAdaptive classifier

4. Contour images analysis

5. Face detection’s approaches

6. Face detection’s approachesSkin color’s cluster

7. Adaptive classifier

8. Adaptive classifier

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

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

11. Algorithm workflow

12. Classifier’s algorithm workflow

13. OWL modelWe 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# interactionWe can use standartXmlDocumentReader to parse OWL model. Also we can use LINQ to parse this model.