Face detection

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Face detection

  1. 1. Face detection<br />Tuck N.<br />
  2. 2. IDEF0 decomposition<br />
  3. 3. Face detection’s approaches<br />Contour images analysis<br />Intensity and colors analisys<br />Adaptive classifier<br />
  4. 4. Contour images analysis<br />
  5. 5. Face detection’s approaches<br />
  6. 6. Face detection’s approaches<br />Skin color’s cluster<br />
  7. 7. Adaptive classifier<br />
  8. 8. Adaptive classifier<br />
  9. 9. AdaBoost<br />AdaBoost is an algorithm for constructing a “strong” classifier as linear combination<br />f(x) =αtht(x)<br />of “simple” “weak” classifiers ht(x) : X -> {−1,+1}.<br />
  10. 10. Rapid Object Detection using a Boosted Cascade of Simple Features<br />Integral Images<br />
  11. 11. Algorithm workflow<br />
  12. 12. Classifier’s algorithm workflow<br />
  13. 13. OWL model<br />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:<br /><owl:Restriction><br /> <owl:onPropertyrdf:resource="#minWidth" /><br /> <owl:minCardinalityrdf:datatype="&xsd;nonNegativeInteger">20</owl:minCardinality><br /></owl:Restriction><br /><owl:Restriction><br /> <owl:onPropertyrdf:resource="#maxWidth" /><br /> <owl:maxCardinalityrdf:datatype="&xsd;nonNegativeInteger">300</owl:maxCardinality><br /></owl:Restriction><br />
  14. 14. OWL and C# interaction<br />We can use standartXmlDocumentReader to parse OWL model. Also we can use LINQ to parse this model.<br />
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