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International Engineering Research Journal (IERJ) Volume 1 Issue 4 Page 106-109, 2015, ISSN 2395-1621
RAPID OBJECT DETECTION BY
USING CASCADE OF BOOSTED
CLASSIFIERS BASED ON HAAR –
LIKE FEATURES
ISSN 2395-1621
Prof. N. V. Puri #1, Aakash Bhavsar #2, Suraj Pawar #3
1
neelam0302@gmail.com
aakask.bhavsar21@gmail.com
3
suraj.pawar220@gmail.com
#123
Computer engineering
Universal College of Engineering & Research, Pune
Pune, India.
2
ABSTRACT
ARTICLE INFO
Viola-Jones introduced a system termed Haar Classifiers -- which was able to detect
frontal faces from a given input images. Since face is the basic identity of a human
being, and many such approaches are developed to detect faces from a given input
image. This paper works on the principle developed by of Viola-Jones . As compared to
other system available our system works on the diplomatic values or data available from
a grey scale image conversion. Because of this grey scale conversion technique a system
works efficiently as compared to other system which on works on pixel configuration.
Article History
Keywords— Haar like features, classifiers, cascading, image processing.
Published online :
Received : 10th April, 2015
Received in revised form :
14th April, 2015
Accepted : 18th April, 2015
22nd April, 2015
I.
INTRODUCTION
A face detecting system must be able to tell
whether a given set of input images contains faces, and if
faces are present their location should be highlighted. For
such detection some process must be done which may scan
input image for faces. For such scanning process classifiers
are developed. These classifiers are beforehand trained to
detect any kind of object.
Proper training of these classifiers may reduce any
kind of false detection. These classifiers are trained using a
series of positive samples as well as negative face samples.
To increase the detection rate of our classifiers, Adaboost
technique or algorithm is used which boost the detection
rate. Along with this integral image, classifiers cascading
technique increases efficiency of Viola-Jones.
II. PROBLEM DESCRIPTION
Our system is developed to detect different objects
as per the training given to classifiers. Main objective of our
system is to detect faces from the given input images. Our
system limits the input images, if it contains faces, faces
should be upright and facing camera.
A typical input image example given to our system
is as shown below. This image contains several upright
faces along with several distinct objects. We are developing
our system in such a way that it should be able to detect
almost all faces available in the given system and each face
should be highlighted at respective locations.
Many systems are available for face detection or object
detection. Each system has their own advantage and each
system is developed to overcome the drawbacks of available
system. Images are collected of different colored pixels.
Pixels are analyzed with variations to differentiate among
themselves.
In 2001, Viola-Jones introduced Haar system consisting
Haar classifiers. These Haar classifiers compare pixels and
helps in detecting faces efficiently.
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International Engineering Research Journal (IERJ) Volume 1 Issue 4 Page 106-109, 2015, ISSN 2395-1621
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Fig 2 : Integral image generation
IV. WHAT ARE HAAR FEATURES &THEIR CLASSIFIERS
Fig 1: Example of an input image. [10]
III. FACE DETECTION FRAMEWORK
In a face detection framework, a input image is scanned
under various classifiers using various scanned images.
Various classifiers try to find faces in the input image as per
training given to them. For a face detection system
approximately 35,000 classifiers are required for a320 x 200
pixel image.
Viol Jones introduced Haar classifiers which were
able to detect faces from a given input image. These
classifiers uses integral image concept for the purpose of
rapid pixel value calculations. This integral image is
required to calculate the value of all pixels underlying each
Haar widow.
To reduce to the rate of use weak classifiers
AdaBoost technique is used. This technique combines all
possible classifiers which are able to give approximately the
correct output called as strong classifiers and neglects the
weak classifiers. To reduced this weak classifiers rate
classifiers are trained using negative samples and positive
samples. This helps in reduction of almost 90% of weak
classifiers.
After successful detection rate of one classifiers the
sub window is passed further to another classifiers. Since
only one classifier is not able to detect the correct face
location various such classifiers are required to detect a face.
To reduced the use of large number of classifier, cascading
of classifiers technique is used.
INTEGRAL IMAGE EPRESENTATION.
Integral image is method of image processing which
helps in rapid calculating the total value of pixel underlying
sub-window. For a pixel at a specific value (x, y)  its
value in integral image representation is the total of all
pixels on leftmost top of that pixel (x, y).
Haar classifiers like rectangular sub- window
which works as per algorithm designed for them. Each
classifier have some individual feature assigned to them. For
example a, two classifier which is divided into parts black
and white as shown figure, has a feature assigned of finding
out eyes location throughout the image. Some classifiers
have the feature of detecting nose, lips region in input image.
Viola – Jones introduced three types of rectangle
classifiers. In two rectangle classifiers which are of same
size and shape, the difference between the rectangle
windows is considered. These two rectangles are vertically
or horizontally adjacent to each other and are differentiated
by black and white regions. A three rectangle classifier is
used which calculates the sum of pixels under the same
colored regions and this sum is subtracted from the other
colored rectangle sub region.
The feature given to a classifier is nothing but the
training given to them. Some of these classifiers are nothing
but the training given to them. Some these classifiers are
composed of two or more numbers of classifiers. Input
image is passed through each classifier for several detection
stages.
Fig 3: Haar like feature patterns.
Working of classifiers- A Haar classifier consider all the
pixel value coming under the respective regions viz, black
and white ,i.e, it sums all the pixel values under black region
and under white regions and the subtracts the two region
values and resulted value is considered as threshold which is
further used for detection purpose.
Each stage contains multiple combination of Haar
classifier with different combinations of classifiers. ViolaJones during the experiments had used 38 stages and n total
6000 classifiers for detecting faces in their input images.
A
B
R
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International Engineering Research Journal (IERJ) Volume 1 Issue 4 Page 106-109, 2015, ISSN 2395-1621
Fig 4: Calculating the area of a rectangle R is done using
the corner of the
4.
Face Detection Evaluation
5.
If face detected, evaluate pixel location
Rectangle D-C-B+A.
CASCADING THE CLASSIFIERS
The process of cascading the classifiers is also
termed as Ada Boost technique. It is the processing
technique which boosts the face detection rate by eliminating
the unwanted classifiers coming into picture during detection
process. Before passing an image to the Haar classifiers
stage, an algorithm is designed in such a way that it uses only
those classifiers which are assumed to give almost high
positive detection rate.
An input image when passed through one classifier
for evaluation and classifier returns a false value evaluation
of that classifier ends and new classifier is selected. If the
classifier returns a true value on evaluation a threshold value
is set and the image is passed on to another classifier and
process continues.
FLOWCHART
Start
Accept image from a
source
Pass
pass
face
Stage 1
Stage n
Stage 2
Image filtering, noise
removal, scaling
Fail
fail
fail
Fig 5: Cascading Stages.
Classifier Choosing
Classifier Cascading
TRAINING OF CLASSIFIERS
To detect a face from a given input image the Haar
classifiers must be a given a special training. Training
algorithm consists several other algorithm such a AdaBoost
algorithm, integral technique, cascading. The Haar
classifiers are trained using positive samples of image
containing face as well as using negative samples which are
not containing faces. Large amount of samples are required
for training purpose. For a better trained classifiers positive
sample images which contain separate facial features are
used. Training of classifiers using such separate facial
features increases the integrity of Haar classifiers.
ALGORITHM
Search for
new
classifier
Face Detection
Evaluation
If face detected,
evaluate pixel
location
The Viola-Jones algorithmic rule uses Haar-like options,
that is, a real between the image and some Haar-like
templates. Where I = image and P = Pattern.
∑ ∑ I(i, j)P(i, j) is white --- ∑ ∑ I(i, j)P(i,j) is black
1≤I≤N 1≤I≤N
1≤I≤N
1≤I≤N
For an image to successfully processed it should be properly
normalized before use. Noisy image are rejected in their
initial stages.
FLOWCHART
1.
Accept image:
2.
Image filtering, noise removal, scaling
3.
Classifier Choosing Classifier Cascading
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End
V. CONCLUSION
We are trying to develop a system which would be able
to detect as possible as all the available faces in the input
image. Instead of using large number of classifiers for
detection we are trying to use scaling technique on input
image instead on classifiers, so that it would reduce the use
classifiers. With this reduced classifiers faster detection rate
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International Engineering Research Journal (IERJ) Volume 1 Issue 4 Page 106-109, 2015, ISSN 2395-1621
and less complexity is achieved. Generated result would be
compared with the available system outputs to show our
system efficiency. Our main concern is to increase our
system efficiency by 30%.
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