!--
18x15 Nose detector computed with 7000 positive samples
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
| By downloading, copying, installing or using the software you agree
| to this license.
| If you do not agree to this license, do not download, install,
| copy or use the software.
|
| Copyright (c) 2008, Modesto Castrillon-Santana (IUSIANI, University of
| Las Palmas de Gran Canaria, Spain).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
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| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions in binary form must reproduce the above
| copyright notice, this list of conditions and the following
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| * The name of Contributor may not used to endorse or promote products
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| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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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]
--><opencv_storage><classifier_Nariz_20stages type_id="opencv-haar-classifier"><size>
18 15</size><stages><_><!-- stage 0 --><trees><_><!-- tree 0 --><_><!-- root node --><feature><rects><_>
8 4 4 4 -1.</_><_>
8 4 4 2 2.</_></rects><tilted>1</tilted></feature><threshold>0.0363217890262604</threshold><left_val>-0.6772649884223938</left_val><right_val>0.6687346100807190</right_val></_></_><_><!-- tree 1 --><_><!-- root node --><feature><rects><_>
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.</_><_>
6 0 3 8 2.</_></rects><tilted>0</tilted></feature><threshold>-0.0794939175248146</threshold><left_val>0.6347042918205261</left_val><right_val>-0.1611918956041336</right_val></_></_><_><!-- tree 4 --><_><!-- root node --><feature><rects><_>
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.</_><_>
10 1 3 4 2.</_></rects><tilted>1</tilted></feature><threshold>0.1654247045516968</threshold><left_val>-0.0291650108993053</left_val><right_val>0.2784962058067322</right_val></_></_><_><!-- tree 6 --><_><!-- root node --><feature><rects><_>
8 1 8 3 -1.</_><_>
8 1 4 3 2.</_></rects><tilted>1</tilted></feature><threshold>0.1449110060930252</threshold><left_val>-0.1593054980039597</left_val><right_val>0.5626019239425659</right_val></_></_><_><!-- tree 7 --><_><!-- root node --><feature><rects><_>
3 0 12 3 -1.</_><_>
3 1 12 1 3.</_></rects><tilted>0</tilted></feature><threshold>-0.0126969404518604</threshold><left_val>-0.6924440860748291</left_val><right_val>0.1042767018079758</right_val></_></_><_><!-- tree 8 --><_><!-- root node --><feature><rects><_>
8 0 2 3 -1.</_><_>
8 1 2 1 3.</_></rects><tilted>0</tilted></feature><threshold>5.2858339622616768e-003</threshold><left_val>0.0736001133918762</left_val><right_val>-0.8135973811149597</right_val></_></_><_><!-- tree 9 --><_><!-- root node --><feature><rects><_>
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><_>
7 1 2 1 -1.</_><_>
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><_>
4 0 9 7 -1.</_><_>
7 0 3 7 3.</_></rects><tilted>0</tilted></feature><threshold>-0.0181720405817032</threshold><left_val>0.4662343859672546</left_val><right_val>-0.1598974019289017</right_val></_></_><_><!-- tree 13 --><_><!-- root node --><feature><rects><_>
3 7 12 8 -1.</_><_>
3 9 12 4 2.</_></rects><tilted>0</tilted></feature><threshold>-0.1194007992744446</threshold><left_val>0.5828961133956909</left_val><right_val>-0.1248269975185394</right_val></_></_><_><!-- tree 14 --><_><!-- root node --><feature><rects><_>
2 0 14 14 -1.</_><_>
9 0 7 14 2.</_></rects><tilted>0</tilted></feature><threshold>-0.4961996078491211</threshold><left_val>0.7593098878860474</left_val><right_val>-0.0939436629414558</right_val></_></_><_><!-- tree 15 --><_><!-- root node --><feature><rects><_>
3 4 12 9 -1.</_><_>
3 7 12 3 3.</_></rects><tilted>0</tilted></feature><threshold>-0.1830939948558807</threshold><left_val>0.5817549228668213</left_val><right_val>-0.0883935913443565</right_val></_></_></trees><stage_threshold>-1.8310650587081909</stage_threshold><parent>-1</parent><next>-1</next></_><_><!-- stage 1 --><trees><_><!-- tree 0 --><_><!-- root node --><feature><rects><_>
5 3 6 1 -1.</_><_>
8 3 3 1 2.</_></rects><tilted>0</tilted></feature><threshold>0.0485280007123947</threshold><left_val>1.5333959890995175e-004</left_val><right_val>-2.6736979980468750e+003</right_val></_></_><_><!-- tree 1 --><_><!-- root node --><feature><rects><_>
9 2 6 4 -1.</_><_>
9 2 3 4 2.</_></rects><tilted>1</tilted></feature><threshold>0.1116186007857323</threshold><left_val>-0.1391783952713013</left_val><right_val>0.4706197082996368</right_val></_></_><_><!-- tree 2 --><_><!-- root node --><feature><rects><_>
9 2 5 6 -1.</_><_>
9 2 5 3 2.</_></rects><tilted>1</tilted></feature><threshold>0.1409423947334290</threshold><left_val>-0.4590255022048950</left_val><right_val>0.6874074935913086</right_val></_></_><_>