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Face recognition technique based on adaptive-opposition particle swarm optimization (AOPSO) and support vector machine (SVM)

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OPSO and AAPSO are the most recently developed face recognition techniques, in order to optimize the parameters of SVM. However, in order to increase the optimization, a combination between OPSO and AAPSO techniques has been proposed in this paper. The proposed technique is called Adaptive-Opposition particle swarm optimization (AOPSO). In AOPSO, the random values in the initial generation of the population in PSO is solved by OPSO and the randomization fixed values in the velocity coefficient is solved using AAPSO in the same time. Then, the proposed algorithm is used with support vector machine to find the optimal parameters of SVM. The performance of the proposed AOPSO method has been validated with two face images datasets, YALE and CASIA datasets. In the proposed method, we have initially performed feature extraction, followed by the recognition of the extracted features. In the recognition process, the extracted features have been employed for SVM training and testing. During the training and testing, the SVM parameters have been optimized with the AOPSO technique. The comparative analysis has demonstrated that, the AOPSOSVM proposed in this study has outperformed the existing PSO-SVM technique.
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VOL. 13, NO. 6, MARCH 2018 ISSN 1819-6608
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FACE RECOGNITION TECHNIQUE BASED ON ADAPTIVE-OPPOSITION
PARTICLE SWARM OPTIMIZATION (AOPSO) AND SUPPORT VECTOR
MACHINE (SVM)
Mohammed Hasan Abdulameer1, DhurghamA.Mohammed1, Saad Ali Mohammed2, Mohammed Al-Azawi3,
Yahya Mahdi Hadi Al-Mayali4 and Ibrahim A. Alameri5
1Department of Computer Science, Faculty of Education for Women, University of Kufa, Iraq
2College of Imam Kadhim for Islamic Sciences University, Iraq
3Oman College of Management and Technology, Oman
4University college of Humanities, Iraq
5Jabir Ibn Hayyan Medical University, Iraq
E-Mail: mohammed.almayali@uokufa.edu.iq
ABSTRACT
OPSO and AAPSO are the most recently developed face recognition techniques, in order to optimize the
parameters of SVM. However, in order to increase the optimization, a combination between OPSO and AAPSO techniques
has been proposed in this paper. The proposed technique is called Adaptive-Opposition particle swarm optimization
(AOPSO). In AOPSO, the random values in the initial generation of the population in PSO is solved by OPSO and the
randomization fixed values in the velocity coefficient is solved using AAPSO in the same time. T hen, the proposed
algorithm is used with support vector machine to find the optimal parameters of SVM. The performance of the proposed
AOPSO method has been validated with two face images datasets, YALE and CASIA datasets. In the proposed method,
we have initially performed feature extraction, followed by the recognition of the extracted features. In the recognition
process, the extracted features have been employed for SVM training and testing. During the training and testing, the SVM
parameters have been optimized with the AOPSO technique. The comparative analysis has demonstrated that, the AOPSO-
SVM proposed in this study has outperformed the existing PSO-SVM technique.
Keywords: face recognition, PSO, AOPSO, SVM.
1. INTRODUCTION
Classification, regression and other learning tasks
can be done using a popular machine learning method
known as Support Vector Machine (SVM) [1]. The SVM
classifier [2, 3] is a supervised learning algorithm, which
depends on statistical learning theory [4, 5]. The SVM
classifier is aimed at determining a hyperplane, which
effectively divides two classes by using training data sets
[6]. It is noteworthy that, SVM is a highly effective
technique, which focuses on the concept of increasing the
margin, or level of separation, in the training data. Several
hyperplanes are available, which facilitates the division
of data for classification. Nevertheless, it is crucial to
select optimal hyperplane, which signifies the largest
separation, or margin, between two classes. SVM looks for
the ideal hyperplane making use of support vectors [7].
The support vectors are the training samples, which
estimate the ideal separating hyperplane; furthermore, they
are the most challenging patterns to classify [8]. This
means that, the support vectors comprise of the data
points, which are nearest to the optimal hyperplane. As
SVM deals with a subset of data points (support vectors)
that are close to the decision boundary, generally the SVM
solution depends on the local features of the data. Over the
last ten years, SVM has triggered the interest of
researchers as a contemporary machine-learning technique
for solving problems in a number of fields, such as, pattern
recognition, bioinformatics and other non-linear problems
with small sample sizes. Fundamentally, SVM has robust
theoretical framework and a superior generalization
capability [9]. In terms of implementation, training an
SVM in classification is similar to that of solving a
linearly constrained quadratic programming (QP) problem,
which utilizes considerable volume of memory and
computation time, when dealing with huge volume of
samples. Even though PSO-SVM [6, 10], AAPSO-SVM
[11] and OPSO-SVM [12] have been widely used to
address the above mentioned problems, still it has a room
of an improvement in term of SVM parameters
optimization. Therefore, for resolve this problem, in this
study we have proposed a AOPSO-SVM method. The rest
of the paper has been organized as follows: the related
work has been discussed in section 2, followed by the
description of our proposed method. The experimental
result and analysis have been described in section 3.
Finally, the conclusions and potential future studies have
been presented in Section 4.
2. RELATED WORK
As discussed above, the PSO technique has been
employed in several fields, such as, video-based face
recognition, face verification and face recognition. In
traditional PSO, the populations are randomly generated,
which results in the random generation of population; this
feature creates an element of doubt about accuracy of
results produced by PSO. Another problem in PSO, the
randomization that is occurring in choosing the velocity
coefficients of PSO which is usually fixed random
number.
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Consequently, a number of studies have
attempted to resolve the above mentioned problems.
Mohammed et al., [12] have proposed OPSO-SVM for the
purpose of minimizing the constraint of randomization in
PSO; the OPSO has been employed to enhance the
parameters of the SVM. The optimized SVM has been
trained for effective face recognition. In another study, and
to address the problem of random values for the
acceleration coefficients in PSO; [11] have proposed
adaptive acceleration particle swarm optimization
(AAPSO). They utilized AAPSO to optimize the
parameters of SVM. In addition, many researchers have
been used PSO as a part of face recognition technique.
Raghavendra et al., [13] have proposed a two-image
fusion scheme for incorporating visible and near infrared
face images (NIR), to enhance the performance of face
verification; for this purpose, they have employed particle
swarm optimization (PSO) to discover an ideal approach
for performing fusion of the visible and NIR sub-band
coefficients. For addressing the dynamic optimization
problem Jean-Francois Connolly et al. [14] have proposed
a dynamic particle swarm optimization for a video-based
face recognition system. The authors have proposed an
incremental learning strategy based on dynamic particle
swarm optimization (DPSO), to develop heterogeneous
ensembles of classifiers (where each classifier fits to a
particle) in response to new reference samples. This
approach has been employed to resolve video-based face
recognition, using an AMCS that includes a pool of fuzzy
ARTMAP (FAM) neural networks, which are used for
classifying facial regions; and the enhanced version of
DPSO, optimizes all FAM parameters, so that the
classification rate has been maximized.
3. THE PROPOSED FACE RECOGNITION
TECHNIQUE
The proposed face recognition technique is
performed in three phases: feature extraction by PCA,
Adaptive-Opposition particle swarm optimization
(AOPSO), and parameters selection for SVM with
AOPSO. These three phases have been performed
repeatedly on the input database face images, and thus, the
face images are recognized more effectively. The three
phases are discussed in Sections 3.1, 3.2 and 3.3. The
basic structure of our proposed face recognition system is
shown in Figure-1.
Figure-1. Structure of the proposed recognition technique based on AOPSO-SVM.
3.1 Feature extraction using PCA
The purpose of the feature extraction is to extract
the information that represents the face. Principal
component analysis (PCA) is one of the popular feature
extraction methods [15]. Using an information theory
approach, PCA has been used for feature extraction in face
recognition [16]. This approach could be proficiently and
easily coded to extract the appropriate information from a
face image. The subspace of the image space extended by
the training face image data has been identified and de-
correlated, using the pixel values. The conventional
representation of a face image has been obtained, by
projecting the face image on to the coordinate system
described by the principal components. Information
compression, de-correlation and dimensionality reduction
have been employed for decision making in the projection
of face images into the principal component subspace.
Dealing with an image as a vector in a highly dimensional
face space mathematically attempts the principal
components of the distribution of faces or the eigenvectors
of the covariance matrix of the set of face images. We
have applied PCA on the training and testing database face
images and have obtained exclusive one dimensional
feature vectors.
3.2 The proposed adaptive-opposition particle swarm
optimization (AOPSO)
Particle swarm optimization (PSO) is inspired by
the social behavior of biological creatures, such as fishes
and birds, which have the ability to group together to work
as a whole to locate desirable positions in a certain area,
e.g., fish searching for a food source. This type of search
behavior is equivalent to searching for solutions of
equations in a real-valued search space [17].PSO emulates
the swarm behavior of individuals who represent potential
solutions in a D-dimensional search space. In the proposed
AOPSO method, the populations are generated randomly
as in the standard PSO and also based on opposite number
(Jabeen et al. 2009). The opposite number generation
process is described as below:
Let be the real number which is generated between
the intervals , then the opposite number is
defined by      
Let      be a point, where   and
based on these points the opposite points are defined
as
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     where
   
By utilizing the aforementioned process, the
opposition based populations are initially generated in
OPSO (Jabeen et al. 2009). After the population
generation, each individual particles fitness value is to be
computed. The fitness value of    and point
  can be replaced by
p
otherwise we will continue
with   . To find the more fit ones the opposite point
and its point are evaluated simultaneously.
Particle
i
is often composed of four vectors:
),,,( 21 D
iiii xxxX
, where
d
i
x
is its position in the dth
dimension;
),,,( 21 D
iiii pbestpbestpbestpbest
,
where
d
i
pbest
is the best position in the dth dimension
that particle
i
has found on its own;
),,,( 21 D
iiii vvvV
, where
d
i
v
is the velocity in the dth dimension; and
),,,( 21 D
gbestgbestgbestgbest
, where
d
gbest
is the global best position in the dth dimension
that all particles have found. Particles in a swarm move
through the search space as follows:
          (1)
  , (2)
and
2
r
are two independent random numbers
uniformly generated in the range [0.1] at each updating
iteration from d=1 to D,
d
i
V
is the velocity of the ith
particle,
d
i
x
is the current position of the particle
i
,
d
i
pbest
is the position of the best fitness value of the
particle at the current iteration and
d
gbest
is the position
of the particle with the best fitness value in the swarm. In
addition,and are the adaptive velocity coefficients
based on the proposed formula Adaptive PSO
(Abdulameer et al 2014) :
  
max
2
minmin
f
f
avg
f
f
+, (3)
  
max
2
minmin
f
f
avg
f
f
+, (4)
where and represent the minimum
and maximum values of,
 
and
 are the
particle minimum, average and maximum fitness values of
the entire population, and  and  represent the
minimum and maximum values of. The proposed
AOPSO algorithm is described in the following flowchart:
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The pseudo code of the proposed AOPSO
For every particle
Initialize particle randomly+ opposite number
END
Do
For every particle in both populations
Compute fitness value and choose the fitter ones form both populations are
chosen as particle.
If the Computed fitness value is better than the best fitness value (pbest) in
history
put current value as the new pbest
End
Select the particle that has best fitness value among all the particles as the
gbest
For every particle
Compute the velocity of particle using equation (1)
Update Particle position using equation (2)
End
Continue while maximum iterations or minimum error criteria is not attained
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3.3 Parameter selection for support vector machine
using adaptive opposition particle swarm
optimization
The SVM parameters  optimized using the
proposed AOSPO method as shown in Figure-2.
Figure-2. SVM parameter optimization using the
proposed AOPSO.
The process of optimal parameter selection by
AAPSO in SVM is as follows:
Step 1: Initially, the particles are generated randomly and
based on the opposite number generationwithin
the interval
],[ yx
. The generated particles are
composed of SVM parameters
i
p
. Then, the
parameters of each particle are initiated,
including position and velocity.
Step 2: The fitness value of every particle is calculated
using Equation. (5). The particles that have the
minimum fitness values are selected as the best
particles as follows:
N
i
ii Cp
1
2
2
1
min
, (5)
Such that
Ni
Nib
y
xp
i
i
i
N
i
ii
,2,1,0
,2,1,
1
.
1
(6)
where,
N
is the size of the training dataset, and C is the
cost function.
Step 3: The
i
pbest
of each particle is updated and
i
gbest
for the domain is updated. Based on
these values, the velocity and position of every
particle are updated using equations (1) and (2).
Step 4: Stop if the current optimization solution is good
enough or if the stopping criterion is satisfied.
4. EXPERIMENTAL RESULTS
The proposed recognition technique has been
experimented in the working platform of MATLAB. The
performance of the proposed AOPSO-SVM technique has
been evaluated using the YALE dataset [19] and CASIA
face dataset [20].
The two datasets were divided into training and
testing datasets. In Yale dataset, there are 15 classes, in
each class there are different images in different
conditions. In the experimental tests, 165 images have
been used in the evaluation process75 images for training
and 90 images for testing.From CASIA database, 500
images have been used for experimentation. In CASIA
database, the images are chosen at five different poses in
different environments and illumination variations. In the
evaluation process, the images in the dataset have been
equally divided for training and testing.
The performance of the proposed technique has
been analyzed by conducting n-fold (n = 10) cross
validation over each datasets, and the corresponding
statistical performance measures are determined. To
perform n-fold cross validation, ten folds of training and
testing datasets are generated by folding operation. The
images are obtained from both databases, and the feature
extraction has been computed using PCA, while the
recognition process has been computed using the proposed
AOPSO-SVM technique. Figure-3 illustrates the sample
face images from the YALE and the CASIA databases.
(i)
(ii)
Figure-3. Sample face images from (i) YALE and (ii)
CASA databases.
To analyze the classification performance, we
have conducted 10 experiments on the Yale and CASIA
datasets. Table-1 and Figure-4 illustrate the results of
classification accuracy obtained for the two dataset. In 10
experiments, our proposed AOPSO-SVM method has
attained higher iris image classification accuracy, as
against the standard PSO-SVM. The average classification
accuracy is 86.9% for Yale dataset, and 88.3% for CASIA
dataset.
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Table-1. The accuracy of PSO and AOPSO based on SVM classification performance results
for the Yale and property CASIA face datasets.
Experiments
Accuracy (%)
PSO-SVM
Accuracy (%)
AOPSO-SVM
Accuracy (%)
PSO-SVM
Accuracy (%)
AOPSO-SVM
Yale dataset
Yale dataset
2.5d dataset
2.5d dataset
1
89
92
89
91
2
81
90
80
85
3
80
85
85
90
4
70
82
87
90
5
82
80
80
85
6
80
87
85
90
7
81
90
82
85
8
82
87
80
85
9
85
86
89
92
10
80
90
85
90
Average
81
86.9
84.2
88.3
Figure-4. Average accuracy values of PSO-SVM and the proposed AOPSO-SVM.
Additionally, the performance of our proposed
AOPSO method has been compared with the performance
of the PSO method, Equation. (5). Based on the results
illustrated in Figure-5, it is evident that, our proposed
AOPSO method has yielded more accurate particles that
have lower fitness values, than those generated by the
conventional PSO method. The high performance result
shows that, our AOPSO method is able to determine the
more accurate SVM parameters.
(i)
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(ii)
Figure-5. Performance of AOPSO and PSO methods
(i) YALE database (ii) CASIA database.
In Figure5, our proposed technique has obtained
more accurate particles that have minimum fitness values,
smaller than those obtained with the conventional PSO
method. Therefore, our AOPSO technique has yielded
more accurate SVM parameters. Figure-5(i) and (ii) show
the fitness value performance for particles used on the
YALE and CASIA face images databases. For all
iterations, the fitness values of the particles of our
proposed AOPSO method are lower than those of the
conventional PSO method. Furthermore, the
computational times for our proposed AOPSO and the
PSO methods are shown in Table-2 and Figure-6.
Table-2. Computation times of proposed AOPSO and
PSO techniques with SVM.
Computational Time (sec)
Images
PSO
AOPSO
1
0.182345
0.133412
2
0.024352
0.019061
3
0.025007
0.020171
4
0.02178
0.014853
5
0.020261
0.017977
6
0.020363
0.020351
7
0.019325
0.017685
8
0.020612
0.018368
9
0.020936
0.01778
10
0.018513
0.017532
Figure-6. Computation times of proposed AOPSO-SVM and PSO-SVM techniques.
5. CONCLUSIONS
In this paper, we had introduced AOPSO based
on SVM to address the limitations of the standard PSO
method. These limitations of PSO: using random values in
the initialization process and choosing velocity
coefficients, which may lead to performance instability?
The optimized SVM, using the AOPSO technique, shows
effective face recognition performance. Two human face
databases, YALE and CASIA were utilized to analyze the
performance of our proposed AOPSO-SVM face
recognition technique. The performance and comparative
analysis results had showed that our proposed AOPSO-
SVM technique has yielded higher face recognition
performance results as against the PSO-SVM method.
Based on the 10 experiments we have proved that, our
proposed AOPSO method has attained higher face image
average classification accuracy. The average classification
accuracy was 86.9% for Yale dataset and 88.3% for ASIA
dataset. In addition, the computation time of proposed
AOPSO was lesser than the conventional PSO by 21%, in
terms of finding the optimal parameters in SVM.
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... They used YALE and CASIA face databases to evaluate the effectiveness of the AAPSO-SVM and they achieved promising results better than OPSO-SVM and PSO-SVM. Recently, Abdulameer et al. (2018) and his collages have proposed AOPSO-SVM which is a combination of OPSO-SVM and AAPSO-SVM in order to achieve high recognition results and they succeed to achieve higher recognition accuracy than over mentioned methods. So, that and in order to achieve higher recognition results, a new f ace recognition technique based on the recent developed method (modified PSO) and (SVM) i s introduced in this study. ...
... It is a supervised learning which means if we given a "labeled training data", the algorithm can produce an optimum hyperplane that classifies the new examples. The hyperplane is a line separating a plane into two parts in 2D dimensional space Abdullah et al. (2017); Abdulameer et al. (2018). The classification procedure of SVM in brief is clarified in Fig. 1. ...
Article
Support vector machine can determine the global finest solutions in many complicated problems and it is widely used for human face classification in the last years. Nevertheless, one of the main limitations of SVM is optimizing the training parameters, especially when SVM used in face recognition domains. Various methodologies are used to deal with this issue such as PSO, OPSO, AAPSO and AOPSO. Nevertheless, there is a room of advancements in this kind of optimization process. Lately, an improved version of PSO is developed which is called modified PSO. In this study, a new technique based on modified PSO, called (Modified PSO-SVM) is proposed to optimize SVM parameters. The proposed scheme utilizes modified PSO to seek the finest parameters of SVM two human face datasets: SCface, CASIAV5 and CMU Multi-PIE face datasets are used in the experiments. Then, a comparison is done with the PSO-SVM, OPSO-SVM and AOPSO-SVM and it showed promising results in terms of accuracy.
... They used YALE and CASIA face databases to evaluate the effectiveness of the AAPSO-SVM and they achieved promising results better than OPSO-SVM and PSO-SVM. Recently, Abdulameer et al. (2018) and his collages have proposed AOPSO-SVM which is a combination of OPSO-SVM and AAPSO-SVM in order to achieve high recognition results and they succeed to achieve higher recognition accuracy than over mentioned methods. So, that and in order to achieve higher recognition results, a new f ace recognition technique based on the recent developed method (modified PSO) and (SVM) i s introduced in this study. ...
... It is a supervised learning which means if we given a "labeled training data", the algorithm can produce an optimum hyperplane that classifies the new examples. The hyperplane is a line separating a plane into two parts in 2D dimensional space Abdullah et al. (2017); Abdulameer et al. (2018). The classification procedure of SVM in brief is clarified in Fig. 1. ...
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Support vector machine can determine the global finest solutions in many complicated problems and it is widely used for human face classification in the last years. Nevertheless, one of the main limitations of SVM is optimizing the training parameters, especially when SVM used in face recognition domains. Various methodologies are used to deal with this issue such as PSO, OPSO, AAPSO and AOPSO. Nevertheless, there is a room of advancements in this kind of optimization process. Lately, an improved version of PSO is developed which is called modified PSO. In this study, a new technique based on modified PSO, called (Modified PSO-SVM) is proposed to optimize SVM parameters. The proposed scheme utilizes modified PSO to seek the finest parameters of SVM two human face datasets: SCface, CASIAV5 and CMU Multi-PIE face datasets are used in the experiments. Then, a comparison is done with the PSO-SVM, OPSO-SVM and AOPSO-SVM and it showed promising results in terms of accuracy.
... They used YALE and CASIA face databases to evaluate the effectiveness of the AAPSO-SVM and they achieved promising results better than OPSO-SVM and PSO-SVM. Recently, Abdulameer et al. (2018) and his collages have proposed AOPSO-SVM which is a combination of OPSO-SVM and AAPSO-SVM in order to achieve high recognition results and they succeed to achieve higher recognition accuracy than over mentioned methods. So, that and in order to achieve higher recognition results, a new f ace recognition technique based on the recent developed method (modified PSO) and (SVM) i s introduced in this study. ...
... It is a supervised learning which means if we given a "labeled training data", the algorithm can produce an optimum hyperplane that classifies the new examples. The hyperplane is a line separating a plane into two parts in 2D dimensional space Abdullah et al. (2017); Abdulameer et al. (2018). The classification procedure of SVM in brief is clarified in Fig. 1. ...
... This work uses modern methods of image processing and image recognition [12,13], probability theory and mathematical statistics [14], theory of elasticity, methods of programming and computer simulation [15]. The deformation of the fingerprint image is determined by a displacement map, which can be interpreted as displaying one image to another image: ...
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The current study is to develop modeling methods, Analysis and synthesis of fingerprints deformations images and their application in problems of automatic fingerprint identification. In the introduction justified urgency of the problem, is given a brief description of thematic publications. In this study will review of modern technologies of biometric technologies and methods of biometric identification, the review of fingerprint identification systems, investigate for distorting factors. The influence of deformations is singled out, the causes of deformation of fingerprints are analyzed. The review of modern ways of the account and modeling of deformations in problems of automatic fingerprint identification is given. The scientific novelty of the work is the development of information technologies for the analysis and synthesis of deformations of fingerprint images. The practical value of the work in the application of the developed methods, algorithms and information technologies in fingerprints identification systems. In addition, it has been found that our paper "devoted to research methods and synthesis of the fingerprint deformations" is a more appropriate choice than other papers.
... This work uses modern methods of image processing and image recognition [12,13], probability theory and mathematical statistics [14], theory of elasticity, methods of programming and computer simulation [15]. The deformation of the fingerprint image is determined by a displacement map, which can be interpreted as displaying one image to another image: ...
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Full-text available
The current study is to develop modeling methods, Analysis and synthesis of fingerprints deformations images and their application in problems of automatic fingerprint identification. In the introduction justified urgency of the problem, is given a brief description of thematic publications. In this study will review of modern technologies of biometric technologies and methods of biometric identification, the review of fingerprint identification systems, investigate for distorting factors. The influence of deformations is singled out, the causes of deformation of fingerprints are analyzed. The review of modern ways of the account and modeling of deformations in problems of automatic fingerprint identification is given. The scientific novelty of the work is the development of information technologies for the analysis and synthesis of deformations of fingerprint images. The practical value of the work in the application of the developed methods, algorithms and information technologies in fingerprints identification systems. In addition, it has been found that our paper "devoted to research methods and synthesis of the fingerprint deformations" is a more appropriate choice than other papers.
... A computer is used to store the bits or binary digit for each pixel in a sequence and it is usually called "compressed" as it is being represented mathematically. The computer read and then interpreted the bits to generate an account of analog to display [3]. The basic steps during the processing of digital image are: image acquisition, image enhancement, image restoration, colour image processing, processing of multi-resolution and wavelets, segmentation, description, recognition and representation of object and morphological processing. ...
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Since there are provisions of many attributes that are not possible or difficult to follow by networks conventionally, mobile ad-hoc networks are extensively deployed. This application starts through the defense sectors, the sensory node presents in the hostile territories down to the gadgets for congestion communication in traffic by general transportation when travelling for adequate provision of infrastructure during disaster recovery. As a lot of importance related to (mobile ad hoc network) MANET application, one important factor in ad-hoc networks is security. Using image processing for securing MANET is the area of focus of this research. Therefore, in this article, the security threats are assessed and representative proposals are summarized in ad-hoc network's context. The study reviewed the current situation of the art for original to security provision called mobile ad hoc network for wireless networking. The threats to security are recognized while the present solution is observed. The study additionally summarized education erudite, talks on general issues and future instructions are recognized. Also, in this study, the forecast weighted clustering algorithm (FWCA) is employed as a cluster head over weighted clustering algorithm (WCA) is examined as quality in cluster-based routing, service is highly significant with MANET.
... Recently, neural networks, genetic algorithm and support vector machine are among the well-known machine learning techniques that are adopted to create a good classifier for fingerprints [3] [4]. Particle swarm optimization is one of the highly common methods in artificial intelligence field [5], and many versions have been exploited with support vector machine as a classifiers methods for human faces [6][7][8] [9]. In this paper, we will use a combined classifier based on "particle swarm optimization (PSO)" and "support vector machine (SVM)" in order to introduce a new classifier able to distinguishing fingerprints powerfully. ...
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Fingerprint is one of the broadly utilized biometric distinguishing proof to recognize the persons due steady and worthiness. The classification of fingerprints gives indexing to the dataset to decrease the seeking and matching manner. There researches have been developed numerous approaches for fingerprint classification model, for example, the Neural Network (NN) methods and support vector machine. Nevertheless, there are a lot of developments to develop the classification procedure. In this paper, a new classification model based on PSO-SVM technique is used to achieve fingerprint classification process. The proposed methodology comprises three phases: preprocessing phases, feature extraction phase and classification phase using the proposed PSO-SVM model. The CASIA V5 fingerprint dataset and a property dataset are adopted in this study to test the proposed model. The experimental results showed that the proposed PSO-SVM classification model was better than the standard SVM in terms of accuracy, sensitivity and specificity for both datasets.
... The lack of recognition of faces high-risk problems, that can be lead to a global ideal makes it a complex problem, so, many of studies have in solutions. Gold and Sollich (2003), Lauer and Bloch (2008) recent years used the techniques of artificial intelligence and Abdulameer et al. (2006). However, the selection to deal with the challenges of facial recognition. ...
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Face recognition is one of the most promising research area in the last decades. The SVM approach is one of the famous approaches in machine learning fields because it can determine the global optimum solutions with lesser number of training samples especially, complex non-linear challenges such as in face recognition applications. Though, there is an important issue that can affects the whole classification process which is picking the optimum parameters of SVM. Recently, Particle Swarm Optimization (PSO) is used to discover the optimal parameters of SVM and many versions of PSO are used for this purpose, like: PSO-SVM technique, opposition PSO and SVM which called (OPSO-SVM) technique and AAPSO-SVM technique which represents a daptive acceleration PSO and SVM. In this study, a new hybrid technique based on the combination of "Accelerated PSO" and "OPSO-SVM" is introduced for face recognition applications. The hybridization can improve the convergence speed in PSO in order to find the optimal parameters of SVM. In the feature extraction process, the PCA algorithm is used for that purpose and the resulted features are delivered to the proposed technique in order to classify the face images. Two human face datasets are used in the experimentation stage such as, SCface dataset and CASIA face dataset in order to validate the performance of the proposed technique. The comparison process for proposed technique with the other recent technique, like: PSO-SVM, OPSO-SVM and AAPSO-SVM is done as an assessment process. The proposed technique provided high accuracy for recognition when we compared it with the other techniques and it was robust in finding the optimal parameters of SVM.
... It accomplishes the classification process through getting the maximum hyperplane that able to exploit the margin between dual classes. The support vectors are used to define that hyperplane [10] [11]. ...
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One of the most important and motivating tasks in computer vision is face recognition. This paper does a comparative study of commonly used classification methods for face recognition tasks (PSO-SVM, AAPSO-SVM and OPSO-SVM). All these approaches are explored and evaluated on two different human faces datasets: FERET and YALE datasets. The proposed methods are investigated via n-fold cross validation in order to notice the overall performance. Moreover, different measures like accuracy, sensitivity and specificity are adopted to evaluate the classification methods performance. The experimental results illustrated that AAPSO-SVM outperform the other classification methods in accuracy, sensitivity and specificity measurements.
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A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.
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