haar——nosecascade

!--
  18x15 Nose detector computed with 7000 positive samples


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| Copyright (c) 2008, Modesto Castrillon-Santana (IUSIANI, University of
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RESEARCH USE:
If you are using any of the detectors or involved ideas please cite one of these papers:


@ARTICLE{Castrillon07-jvci,
  author =       "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Tejera, M. and Guerra Artal, C.",
  title =        "ENCARA2: Real-time Detection of Multiple Faces at Different Resolutions in Video Streams",
  journal =      "Journal of Visual Communication and Image Representation",
  year =         "2007",
  vol =          "18",
  issue =        "2",
  month =        "April",
  pages =        "130-140"
}


@INPROCEEDINGS{Castrillon07-swb,
  author =       "Castrill\'on Santana, M. and D\'eniz Su\'arez, O. and Hern\'andez Sosa, D. and Lorenzo Navarro, J. ",
  title =        "Using Incremental Principal Component Analysis to Learn a Gender Classifier Automatically",
  booktitle =    "1st Spanish Workshop on Biometrics",
  year =         "2007",
  month =        "June",
  address =      "Girona, Spain",
  file = F
}


A comparison of this and other face related classifiers can be found in:


@InProceedings{Castrillon08a-visapp,
 'athor =       "Modesto Castrill\'on-Santana and O. D\'eniz-Su\'arez, L. Ant\'on-Canal\'{\i}s and J. Lorenzo-Navarro",
  title =        "Face and Facial Feature Detection Evaluation"
  booktitle =    "Third International Conference on Computer Vision Theory and Applications, VISAPP08"
  year =         "2008",
  month =        "January"
}


More information can be found at http://mozart.dis.ulpgc.es/Gias/modesto_eng.html or in the papers.


COMMERCIAL USE:
If you have any commercial interest in this work please contact 
[email protected]
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                  8 4 4 4 -1.</_><_>
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                  3 0 12 7 -1.</_><_>
                  6 0 6 7 2.</_></rects><tilted>0</tilted></feature><threshold>0.0544859282672405</threshold><left_val>-0.4403176903724670</left_val><right_val>0.4891850948333740</right_val></_></_><_><!-- tree 2 --><_><!-- root node --><feature><rects><_>
                  3 5 12 9 -1.</_><_>
                  3 8 12 3 3.</_></rects><tilted>0</tilted></feature><threshold>-0.1508972942829132</threshold><left_val>0.6370239257812500</left_val><right_val>-0.2814675867557526</right_val></_></_><_><!-- tree 3 --><_><!-- root node --><feature><rects><_>
                  6 0 6 8 -1.</_><_>
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                  3 8 12 4 -1.</_><_>
                  3 10 12 2 2.</_></rects><tilted>0</tilted></feature><threshold>-0.0670417398214340</threshold><left_val>0.5956599712371826</left_val><right_val>-0.1645421981811523</right_val></_></_><_><!-- tree 5 --><_><!-- root node --><feature><rects><_>
                  10 1 3 8 -1.</_><_>
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                  5 6 11 9 -1.</_><_>
                  5 9 11 3 3.</_></rects><tilted>0</tilted></feature><threshold>0.1319603025913239</threshold><left_val>-0.0852369293570518</left_val><right_val>0.6464285850524902</right_val></_></_><_><!-- tree 10 --><_><!-- root node --><feature><rects><_>
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                  8 1 1 1 2.</_></rects><tilted>0</tilted></feature><threshold>2.6259789592586458e-005</threshold><left_val>-0.2522526085376740</left_val><right_val>0.2770084142684937</right_val></_></_><_><!-- tree 11 --><_><!-- root node --><feature><rects><_>
                  9 1 2 1 -1.</_><_>
                  9 1 1 1 2.</_></rects><tilted>0</tilted></feature><threshold>8.9456392743159086e-005</threshold><left_val>-0.1598252952098846</left_val><right_val>0.1796030998229981</right_val></_></_><_><!-- tree 12 --><_><!-- root node --><feature><rects><_>
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