International Research Journal of Computer Science and Information Systems (IRJCSIS) Vol. 1(2) pp. 27-31,
December, 2012
Available online http://www.interesjournals.org/IRJCSIS
Copyright © 2012 International Research Journals
Full Length Research Paper
Face recognition based on statistical
approaches, neural networks and support
vector machines
M. Er-raoudi, *M. Fakir, B. Bouikhalene
Information Processing and Telecommunications Teams, Faculty of Sciences and Technics, Sultan Moulay
Slimane University, Béni Mellal, Morocco
Accepted 23 November, 2012
In this work, a face recognition method based on multilayer neural networks (MNC), the K-nearest
neighbor and support vector machines SVM is proposed. Features are extracted using principal
component analysis (PCA), the moments of Hu and Legendre. Evaluation was performed on 200 images
of ORL database including 40 individuals and 5 images for each individual.
Keywords: Face recognition, principal component analysis, support vector machines, the K-nearest neighbor,
multilayer neural networks, Hu moments and Legendre.
INTRODUCTION
Currently, authentication and identification of individuals
is very important especially in the security field, in this
area face recognition is responsible for identification and
authentication. The problem of face recognition is defined
as follows: given a face image, we want to determine the
identity of that person.
A recognition system comprises two essential steps:
the feature extraction followed by classification. In this
work, we will make a comparative study of face
recognition based on statistical approaches, neural
networks and support vector machines.
Pretreatments
The image can be affected by various factors causing its
deterioration, it can be noisy, ie contain hash because of
optical or electronic devices.
That is why in the pretreatment must remove noise by
processing techniques and image restoration, this
operation is very complex. There are several methods of
processing and image enhancement, such as:
standardization
(http://www.tsi.telecom-paristech.fr
/
pages/enseignement/ressources/beti/acp/effetde.htm),his
togram equalization
*Corresponding Author E-mail: fakfad@yahoo.fr
(http://autoformation.freehostia.com/base/Imagerievideo/traitement-images.htm),
filtering
(http://xphilipp.developpez.com/articles /filtres/).
In this work, to improve the quality of images,
normalization and histogram equalization are used as
preprocessing.
Standardization
Dynamics in a grayscale image is the interval
corresponding to the grayscale smallest and largest.
Standardization is to obtain the maximum dynamic range
[0,255].
that is a transformation T: [a, b]
Histogram equalization
It consists in applying a transformation on each pixel of
the image, and thus to obtain a new image from an
independent operation of each of the pixels. This
transformation is constructed from the cumulative
histogram of the original image. Figure (1) shows the
effect of equalization method is fast, easy
implementation, and fully automatic.
28 Int. Res. J. Comput. Sci. Inform. Syst.
Figure 1. Histogram equalization
Feature extraction
This is the key step of the appointment, since the
performance of the entire system depends. In this step
also known as indexing or modeling, we extract the face
image information that enables to model the face of a
person by a measurement vector that characterizes and
distinguishes it:
(HMM: Hidden Markov Model).
3. The Linear Discriminant Analysis LDA: also known as
the'''' Fisherfaces (Yang et al., 1999), which reduces the
dimensionality of space by optimizing the discrimination
factor between classes and two-dimensional version
ADL2D (Visani et al., 2004).
There are other algorithms that were developed using the
discrete transforms such as Discrete Cosine Transformed
(DCT) and Discrete Wavelet Transformed (DWT) and
others based on moments.
Geometric (local) Methods
They are also called methods in facial features, local
features, or analytical. The analysis of the human face is
given by the description of its individual parts and their
relationships
Hybrid methods
Hybrid methods are approaches that combine global and
local features to improve performance of face recognition.
Indeed, local features and global features have properties
(Behzad and Gholam, 2011).
Global methods
This class contains methods that enhance the overall
properties of the form. The face is treated as a whole.
Among the approaches which have been brought
together in this class are:
1. The principal component analysis PCA: also known as
the'' eigenfaces'' (Sirovich and Kirby, 1987; Sirovich and
Kirby, 1990; Turk and Pent land, 1991; Yang et al.,
2004), which is a statistical projection technique to extract
a small space under optimal ACP2D dimensional version
(Yang et al., 2004).
2.The Stochastic Approach: Samaria (Mohamed and
Djallel, 2002) argues that when the frontal images are
scanned from top to bottom there is a natural order in
which the features appear, and this can be modeled in a
practical manner using a model of hidden Markov Models
CLASSIFICATION METHODS
The face recognition systems have a classification step in
which several classifiers were adopted. Among which,
there is neural networks (Hardy, 2005), Hidden Markov
Models
(HMM)(http://en.wikipedia.org/wiki/Hidden_Markov_mode
l)and vector machines separators (Antoine Cornuéjols.
Une nouvelle méthode d’apprentissage: Les SVM.
Séparateurs à vaste marge. Synthèse: Université
deParis-Sud, Orsay, juin, 2002), classifiers and Bayesian
approaches
(http://fr.wikipedia.org/wiki/Recherche_des_plus_proches
_voisins) (Hastie, 2001) based on the Euclidean distance
to nearest neighbors.
Er-raoudi et al. 29
Figure 2. Architecture of a neural network for face recognition
Figure 3. Transformation of a non linear problem
into a linear problem
Neural networks
A neural network is a parameterized function which is the
composition of simple mathematical operators called
formal neurons. It is a technique inspired by biological
neural networks to perform complex tasks in different
application types (classification, identification, character
recognition, voice, vision, control system, face recognition
... etc.).
The field of face recognition, neural networks is used
as machine learning and recognition, the architecture
used is "M RN" (multilayer neural network) is typically
used. To start, a raw image (or pretreated) of fixed size is
usually the input source networks. Dimensions must be
established beforehand because the number of neurons
in the input layer depends on it.
More dimensions of the image, the greater the
complexity and learning time increases. Indeed, for an
image of dimensions 130 × 150 pixels, 19,500 neurons
will be required on the input layer, which is huge.
Learning effective (i.e. convergence) of such a network is
also doubtful. The number of outputs of the network also
depends directly on the amount of people to discriminate.
It is therefore clear that incremental learning is difficult
and will require adjustments to direct the architecture of
the need for methods to reduce the size of the input
vector (figure 2).
Support vector machine (SVM)
Support vector machines or margin (Antoine Cornuéjols,
2002) are a set of supervised learning techniques to
solve problems of discrimination and regression. They
consist of two or more separate sets of points by a hyper
plane.
To overcome the disadvantages of non-linearly
separable case, the idea of SVM is to change the data
space. The transformation Ф nonlinear data can allow
linear separation examples in a new space. So we will
have a change of dimension. This new dimension is
called "space re-description 'large with a scalar product
(Hilbert space).
Indeed, intuitively, the higher the dimension of the
space re-description, the greater the probability of finding
a separating hyper plane between examples is high. This
is illustrated by the following scheme:
There is therefore a transformation of a non-linear
problem of separation in the space of representation in a
problem of separation in a linear space re-description of
largest
dimension
(Figure
3).
This
nonlinear
transformation is done via a kernel function
30 Int. Res. J. Comput. Sci. Inform. Syst.
Figure 4. The optimal separating hyperplane is the one
that maximizes the margin
Figure 5. The ORL database
(Mohamadally, 2006). In practice, few families of kernel
functions are configurable and it is a known user of SVM
to perform testing to determine which is best suited for its
application. Include the following examples of kernels:
polynomial, Gaussian, Laplacian and sigmoid (figure 4).
K-nearest neighbor’s k-NN
It consists in taking into account (in the same way) the k
training samples whose input is closest to the new input
x, by a distance to be defined.
RESULTS AND DISCUSSION
ORL database
The experiments were based on ORL database, it
contains 40 individuals, and each with 5 images in total
contains 400 images. The Figure 5 illustrates the faces
images of ORL database.
For learning, we took 200 images of ORL database in
total including 40 individuals and each individual was 5
images. The same for the test we took the 40 people
taken into learning with 5 images each, in effect test
Er-raoudi et al. 31
Table 1. Results obtained
Méthode
Taux de la RDV
ACP
75.75%
K-PPV
90%
images and learning are different. The results obtained in
this work are summarized in table 1:
In this section we have used neural networks and SVM
as powerful tools for automatic face recognition which is
justified by the recognition rate of 94% for RN and 99%
for SVM. We also used methods: K-NN, ACP, ACP-RN,
RN-Hu and Legendre-RN. The k-NN(K-PPV) method
performs well with a rate of 90%, it is easy to use, it does
not require learning. Against the methods by ACP, Hu
and Legendre-RN-RN are less efficient (Table 1).
CONCLUSION
In this work we have started a prototype of a face
recognition system which consists of three types of
approaches.
The first approach is based on the k-nearest neighbor
method (K-NN) and the ACP by an extraction using the
ACP and the moments of Hu which has given good
results.
In the second approach we exploited the property of
automatic face recognition neural networks whose
learning is supervised.
The third method is the support vector machine, which
remains the best method proposed in this work.
In perspective, we propose to use a hybrid method by
combining these methods and take large databases to
evaluate better the performance of our system by varying
the conditions. We also propose to apply this system to
video and use fuzzy logic.
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