Transcript Implementation and Validation of Fixed

Detecting mouth with an AdaBoost
cascade based on Haar-like features
P.Viola, M.Jones Robust Real-time
Object Detection.
Second International Workshop on
statistical and computational theories
of vision – Modeling, Learning,
computing and sampling. 2001
Santa Rossi
(GET-ENST)
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Advantages using OpenCV Haar object
detection
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Face detector already implemented
Its only argument is a xml file
Detection at any scale
Face detection at 15 frames per second for
384*288 pixel images
90% objects detected
10-6 false positive rate (FAR)
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Integral Image
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The value of the integral
image at point (x; y) is the
sum of all the pixels above
and to the left.
Integral Image
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The sum of the pixels which
lie within the white
rectangles are subtracted
from the sum of pixels in the
grey rectangles.
Example rectangle features
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Adaboost classifier
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Selects a small number
of critical visual
features
Combines a collection
of weak classification
functions to form a
strong classifier
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The first and second features
selected by AdaBoost for face
detection
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First and second features selected for
mouth detection
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Cascade
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Stages in the cascade are constructed by training
classifiers using Adaboost
Training this detector takes weeks but it is done
once and for all. Then, it processes 15 frames per
second
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Experiments
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BANCA database
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WM + G1 as training
dataset: 3420 mouth
images
Negative examples =
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WM + G1 (face – mouth)
images
WM + G1 (bottom part of
the face – mouth)
images
G2 test data set: 3117
images
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Experiments
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First experiments:
 2 weeks to train
 16 stages cascades
 First results on training
data
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Cascade 1
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Cascade 2
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Pos=93.71 %
First results on test data
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Cascade 1
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Pos=94.42 %
Cascade 2
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Pos=92.25 %
Pos=95.77 %
Eyes permissive !
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Xml file
<?xml version="1.0"?>
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<opencv_storage>
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<detect_bouche_demi_face_20_20 type_id="opencv-haar-classifier">
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<size>
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20 20</size>
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<stages>
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<_>
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<!-- stage 0 -->
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<trees>
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<_>
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<!-- tree 0 -->
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<_>
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<!-- root node -->
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<feature>
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<rects>
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<_>
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0 2 20 12 -1.</_>
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<_>
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0 5 20 6 2.</_></rects>
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<tilted>0</tilted></feature>
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<threshold>-0.0563905797898769</threshold>
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<left_val>0.9257497191429138</left_val>
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<right_val>-0.9348185062408447</right_val></_></_>
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<_>
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<!-- 2005
tree 1 -->
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Perspectives
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Test G2
Add negative samples
Change negative samples process
This detector has a high acceptance rate, we
can add a verification test to validate the
detected object
Implementation on PDA
Learn and test with Qtek database
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