Inter-Communication Classification for Multi

The International Arab Journal of Information Technology, Vol. 11, No. 4, July 2014
387
Inter-Communication Classification for MultiView Face Recognition
Chouaib Moujahdi1, Sanaa Ghouzali1, 2, Mounia Mikram1, 3, Abdul Wadood4, and Mohammed Rziza1
1
LRIT (Associated Unit with the CNRST), Mohammed V-Agdal University, Morocco
2
Information Technology Department, King Saud University, Saudi Arabia
3
The School of Information Sciences, Mohammed V-Agdal University, Morocco
4
Computer Engineering Department, King Saud University, Saudi Arabia
Abstract: In this paper we present a new multi-view face recognition approach. Besides the recognition performance gain
and the computation time reduction, our main objective is to deal with the variability of the face pose (multi-view) in the same
class (identity). Several new methods were applied on face images to calculate our biometric templates. The Laplacian
Smoothing Transform (LST) and Discriminant Analysis via Support Vectors (SVDA) have been used for the feature extraction
and selection. For the classification, we have developed a new inter-communication technique using a model for the automatic
pose estimation of the head in a face image. Experimental results conducted on UMIST database show that an average
improvement for face recognition performance has been obtained in comparison with several multi-view face recognition
techniques in the literature. Moreover, the system maintains a very acceptable running time and a high performance even in
uncontrolled conditions.
Keywords: Face recognition, multi-view, inter-communication, LST, SVDA, pose estimation.
Received July 26, 2012; accepted March 19, 2013; published online April 4, 2013
1. Introduction
Facial recognition is a biometric technique based on
physiological/behavioral characteristics specific to each
person. These characteristics have the advantage that
they are universal, unique, permanent and cannot be
falsified, unlike conventional techniques such as
passwords and badges that can be used fraudulently by
others. For the human brain, the process of face
recognition is a high-level visual task. Although
humans can detect and identify faces in a scene without
much trouble, to build an automatic system that
performs such tasks represents a serious challenge. This
challenge becomes greater when the conditions of
image acquisition are highly variable and
uncontrollable. There are two types of variations
associated with face images: inter- and intra-subject.
The inter-subject variation is limited because of the
physical
resemblance
between
individuals.
Contrariwise, the intra-subject variation is larger and it
can be attributed to several factors: Variations in facial
expressions, lighting conditions, occlusions, unwanted
noise, affine distortions and clutter, etc, may give some
bad impact on the overall performance of face
recognition accuracy. But in general, these problems
are solved with a very high percentage [4, 9, 10, 24].
However, multi-view face recognition, as shown in
Figure 1, remains a major challenge for building a
robust/ reliable biometric system, able to achieve high
recognition rate. This problem has attracted the efforts
of researchers in recent years and is the objective of
our research as well. This paper presents some
development results of a new multi-view face
recognition approach. In this work, several new
approaches are used. A very useful approach to build
our proposed technique of inter-communication
between classifiers is a model for pose estimation to
specify the angle of view in a face image [1]. The
other is the Laplacian Smoothing Transform [12],
used to extract low frequency features, followed by
Discriminant Analysis via Support Vectors to reduce
the dimensionality of the extracted feature vectors
[13].
The rest of the paper is organized as follows:
Section 2 presents an overview of multi-view
approaches. Section 3 describes the techniques of
feature selection LST and SVDA and the proposed
approach. Experimental results are discussed in
section 4, conclusions and perspectives are drawn in
section 5.
2. Overview of Multi-View Approaches
Multi-view face recognition remains a major
challenge to build a robust recognition and a reliable
identification. This problem is a subject of active
research in the fields of pattern recognition and
computer vision. It has attracted research efforts both
because of its potential applications and the challenge
it presents. To ensure robust multi-view face
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recognition with high recognition rate, some
remarkable approaches are proposed in [7, 8, 15, 19].
According to the used technique, we can divide these
approaches into three main categories: Feature-based
approaches, classification-based approaches and hybrid
approaches.
Figure 1. Example of the pose variation from UMIST database.
2.1. Feature-Based Approaches
We can distinguish two type of feature-based
approaches: linear and non-linear approaches. The
linear approaches are based on methods that rely on
statistical properties and use linear algebra. [19]
presents a work based on a linear approach where a
new multi-view database is created, using a simple
acquisition system which consists of five cameras able
to capture simultaneously five views of a human face
with different observation angles. In this work, feature
selection was applied in the spatial domain using
traditional linear/statistical techniques: Principal
Component Analysis (PCA) [29] followed by
Independent Component Analysis (ICA) [2] on the
training and test images. The results shown in this study
prove that these methods are ineffective for non-linear
problems. Some studies have shown that the human
visual system is more sensitive to variations in the lowfrequency band, and that face recognition performance
can be increased in the frequency domain [6]. This has
led researchers to work in the frequency domain. In
[15], a multi-view face recognition based on Gabor
wavelet transform is presented. The face images of
multiple views are convolved with a bank of Gabor
wavelet filters. These convolutions produce high
dimensional feature vectors (Gabor faces). The
dimensionality reduction of Gabor faces is achieved by
Linear Discriminant Analysis (LDA) [16]. Unlike PCA
algorithm, LDA performs a true separation of classes to
reduce the space of facial features in a low dimensional
space. However, the use of LDA, which has several
problems and weaknesses (see subsection 3.1.2.), can
degrade the overall multi-view system performance.
Multi-view face recognition is a non-linear multiclass problem, several studies have shown that linear
methods are inefficient for such problems. In recent
years, other researchers have tried to develop
techniques like Kernel-PCA (KPCA) [27] and Kernel
Direct Discriminant Analysis (KDDA) [20] to make the
distribution patterns of faces simple and linear,
consequently, making them applicable to non-linear
problems. These techniques combine the strengths of
traditional linear methods and kernel machines to
solve non-linear problems. In [21], KDDA with an
RBF kernel (Radial Basis Function as kernel) was
used for feature selection. However, these kernel
methods can not always effectively address the Small
Sample Size (SSS) problem [17] and their
classification process is very slow because of the
computations in the kernel machine. Thus, their results
are still modest. In [7], a new non-linear method is
proposed: Support Vector Machines-Discriminative
Feature Selection (SVM-DFS), to select most
discriminative features without linearly combining the
original features. SVM-DFS proved its robustness
compared to KPCA and KDDA. This approach
reduces the computational time using a linear kernel
but with a degradation of recognition rate and it keeps
a higher recognition rate by using an RBF kernel
while increasing the test time. Thus, it is clear that
SVM-DFS cannot find a better compromise between
performance and computation time.
2.2. Classification-Based Approaches
Feature based approaches in the previous section
focused on the extraction of significant features
whereas the classification process has been conducted
using either K-Nearest Neighbors (KNN) or Support
Vector Machines (SVM) classifiers. Other researchers
have focused on the development of new classification
methods to solve the problem of multi-view face
recognition, which it is a multi-class classification
problem. The SVM has been successfully applied to
various classification problems, but several large-scale
problems are overly difficult to solve using traditional
SVMs. In [8], multi-class SVMs [26] was used to
produce a new method: Min-Max Modular SVMs
(M3-SVM) for multi-view face recognition. In general,
the working procedure of M3-SVM consists of three
stages. The complicated problem of multi-view face
recognition was decomposed into several relatively
simpler two-class sub-problems, and each subproblem is transformed into a binary problem using
the one-versus-one strategy (tasks decomposition
step). For each sub-problem an SVM based
Discriminative Feature Selection (SVM-DFS) method
[7] was used (sub-classification step). Finally, the
combination of sub-solutions strategy follows the
minimization and the maximization principles [22]
(solutions reassembly step). The results obtained in
this study are still modest.
2.3. Hybrid Approaches
Some works in multi-view face recognition make use
of more than one basic approach using together
feature techniques (linear and non-linear) and
Inter-Communication Classification for Multi-View Face Recognition
classification techniques simultaneously. We refer to
such techniques as hybrid approaches. The objective of
the work [28] is to create a new generative model,
which can handle both linearity (identity) and nonlinearity (view) of face images in multiple views,
combining manifold learning [18] and tensor analysis.
The major problem with this approach is that the
treatment of the non-linearity of the view manifolds can
influence the identity information, which can degrade
the overall performance of the system. To solve this
problem, an algorithm to calculate a heuristic parameter
useful for facial recognition was proposed in [11]. The
experiments were performed on Weizmann and oriental
face database. This approach has proved its robustness
over the traditional tensor-based methods like
TensorFace [31] and VPCA [25], but it did not improve
upon the results of other approaches [7, 15].
3. Proposed Approach
In this section, we present a hybrid approach of multiview face recognition based on new methods of feature
selection and a technique of inter-communication of
classifiers using a model of pose estimation in 2D
images.
3.1. Feature Selection
To build robust learning models, feature selection is
used to extract the most relevant features and remove
the most redundant. Motivated by previous studies [6],
we have decided to work in the frequency domain. For
feature selection, we applied the efficient feature
extraction method LST [12] followed by the multi-class
dimensionality reduction approach SVDA [13].
3.1.1. Laplacian Smoothing Transform
Gu et al. showed in [12] some drawbacks of statistical/
manifolds [33, 34] methods resulting due to the
limitation of each pixel being considered independent
and the links with its neighbors are not considered. To
cope with these limitations, Laplacian Smoothing
Transform (LST) is used to represent the image in the
frequency domain. Thus, the high frequency of the
image (containing noise) can be eliminated while
keeping critical data that are represented by low
frequencies and keeping the connection between the
pixels. Compared to Discrete Cosine Transform (DCT)
[32] and Discrete Wavelet Transform (DWT) [5]
methods with respect to efficiency and speed, LST has
proven its robustness/priority [12]. For the LST, the
computational complexity lies in the number of
eigenvectors of the Laplacian matrix L (Expression
3) to be calculated for images of size M×N. However,
the Laplacian matrix L which is a sparse matrix can
be calculated quickly and once for training. Generally,
the LST can be an effective method for feature
extraction in face recognition. The stages of
development of LST are:
389
• Calculate the weight matrix W of size MN× MN (M
and N are the dimensions of the image):
1 if x − x' + y − y' = 1

W ( x , y , x' , y' ) = 
'
'
1 if x − x + y − y ≠ 1
Where x , y = x × N + y
(1)
x and y are the coordinates of a pixel.
• Calculate D, a diagonal matrix whose entries are
column (or row, since W is symmetric) sums of W:
D( x, y, x' , y' ) = ∑W ( x, y, x' , y' )
x, y
(2)
• Calculate the Laplacian Matrix:
LMN = D − W
(3)
• Compute first k eigenvalues and the k
corresponding eigenvectors Ek (k depends on the
number of adequate low frequencies).
• Project the training matrix F (which contains
training images: one image in each row) in the
space spanned by the eigenvectors Ek to build the
training set of signatures G after pre-processing
with LST:
G = E kT F
(4)
• Similarly, each test image f is proposed to construct
the signature test g after preprocessing by LST:
g = E kT f
(5)
3.1.2. Discriminant Analysis via Support Vectors
Many studies have shown that the statistical methods,
such as LDA, in the frequency domain would improve
the recognition rates [34]. However, LDA has several
problems, for example it suffers from the problem of
Small Sample Size (SSS) and it creates subspaces that
favor classes well separated from those which are not.
Thus LDA fails to obtain the optimum direction for
separating two classes in all situations. Alternately, the
SVM can discover the optimal directions to maximize
the margin between two classes [30]. Hence, the idea
to combine LDA with SVM to build a robust multiclass approach for dimensionality reduction:
Discriminant Analysis via Support Vectors (SVDA).
First, SVM is used to calculate an optimal direction to
discriminate between two classes, then the criteria of
class separability (similar to LDA) are calculated
using the distinct Support Vectors (SV). Finally, the
projection matrix is calculated. The general stages of
SVDA development are:
• For every two classes cl and cm, 1 ≤ l < m ≤ M
(number of classes), an SVM is employed to find
an optimal direction clm. At the end of this
procedure, we will have a matrix Φ, which contains
M ( M − 1 ) optimal directions clm (a direction by
2
column).
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The International Arab Journal of Information Technology, Vol. 11, No. 4, July 2014
• The between-class matrix Vb is given by:
Vb =
∑
(6)
T
c lm c lm
= ΦΦ T
1 ≤i < j ≤ M
• Similar to LDA, the within-class matrix Vw is given
by:
M
(7)
V =
( x̂ − µˆ )( x̂ − µˆ )T
w
∑∑
m =1 i∈Î m
i
m
i
m
x̂ i : The vectors of the data matrix of SVs.
µ̂ m : The average of SVs in the class cm.
Î m : Indices of Support Vectors in the class cm.
• Searching for the optimal projection by solving the
equation:
V b α = βV w α
(8)
α: The set of eigenvectors.
β : The set of eigenvalues.
• Eigenvectors corresponding to the largest k'
eigenvalues form the columns of the final
transformation matrix.
In summarize, we presented the techniques used for
feature selection: LST followed by SVDA. For
classification, we propose the technique of intercommunication classification. To apply this technique
we need a face pose estimator. In the next axis, we
present the estimation model used in our work.
3.2. Pose Estimation
The automatic pose estimation of faces in a 2D image is
usually done in two stages: first detect the face and then
the pose of the face is estimated. There are several
different approaches to solve this problem, which can
be divided into three categories [3]: Geometric methods
[23], genetic methods [14], and learning methods [1].
The first category requires the use of 3D information
provided by the image sensor to estimate the pose, but
it is difficult for our work and all available multi-view
databases do not provide this information. In the second
category, the pose will not be calculated in real time
given the high complexity of genetic algorithms.
Therefore, this class is not suited to our needs. For the
latter category, it uses the technique of artificial
learning of 2D images. In this case, a sufficiently large
set of face pictures (with different poses from -90° to
+90°) must be presented to the system during a learning
phase. Once the learning phase is complete, the system
should be able to estimate the pose of a given test
image. Generally, this category has a running time quite
acceptable.
In this work we used the pose estimation model
given by [1]. This model is a learning-based method
which treats the problem of pose estimation as a
regression problem and not as a classification problem
by assigning the face to a class of many poses. In
addition, this model works on images of wide
variations in the background, lighting and expression.
We can summarize the principle and the general
operation of the model in four steps:
• The test image (of size 60 × 60) is divided into a
grid of blocks of size 10 × 10 (i.e., divided into 36
blocks).
• A library of facial images (240 images), with a
range of poses from -90° to +90°, is used. The
library can be regarded as a palette from which the
image blocks can be taken. These blocks provide
information about the true pose.
• The library is used to approximate each block of the
test image. Finally, an approximated image is
formed.
• Pose parameters W are used to interpret the
selected blocks using the Bayesian posterior
probability:
Pr ( β | Y , W ) =
∏iP=1 Pr ( yi , l* | β , ωi ) Pr ( β )
Pr ( Y )
(9)
β: The pose and Y is the test image, Y=[y1, …, yp]
(decomposed into P blocks).
l*: The block of the library that is closest to the test
block yi.
ωi: Denotes the vector of parameters associated with
the ith: Block of the test image and all the blocks in
the library. For more details about the construction of
W you can consult [1].
One of the drawbacks of this model is that it cannot
estimate view angles, greater than 70° and lower than 70°, with high accuracy. However, in practice, this
estimation is sufficient for our needs. Our proposed
inter-communication technique (especially the angles
quantization strategy) makes the incorrect estimations
bearable.
3.3. Inter-Communication Classification
In multi-view face recognition as shown in Figure 2,
sometimes the distance Y between the images (with
different views) of a same identity is greater than the
distance X between the images of the same class of
view (with different identities), which requires
treatment of the variability of the view in the same
class (identity). It is the purpose of our proposed
technique.
After building the training and the test databases,
we apply the LST followed by the SVDA for the
feature selection to extract comparison signatures
associated with each image. For classification, we
implement an inter-communication technique using
the model of pose estimation developed by
Aghajanian and Prince [1]. Since the angles are
estimated like real values in a very wide range, we
have applied a quantification technique of the results,
as shown in Figure 3, which has led to six classes of
Inter-Communication Classification for Multi-View Face Recognition
θ1 =θ-0°= 20° and θ2 =θ-15°= 5°
θ2 =θ-30°= 20° and θ4 =θ-45°= 25°
θ5 =θ-60°= 40° and θ6 =θ-75°= 55°
angles (0°, 15°, 30°, 45°, 60°, and 75°). The test
signature is compared to the training signatures already
prepared using six KNN classifiers. Each classifier has
a class of angles responsible for comparing the test
signature with the training signatures that belong to its
class and returns a distance. We use a classification
with a weighted majority vote on the six obtained
distance values.
45°
• In this case, the minimum distance is reached for
the classifier 15°. The order of priority of classifiers
is as follows: Classifier 15°, classifier 30°,
classifier 0°, classifier 45°, classifier 60° and
classifier 75°.
• The weight of each classifier is calculated using the
following expression:
30°
60°
15°
Identity 1
75°
391
0°
Pi = 1 −
X<Y
X
for i ∈ [ 1, 6 ]
(10)
j
• Finally, we organize a majority vote. For classifiers
that return the same subject class, we sum its
weight. The largest vote favors its identity (subject
class) as a final result.
Identity 2
Figure 2. The View variations of two different identities and
comparison between two distance types (X and Y) in multi-view
face recognition.
It is to be noted that the proposed approach is valid
with any type of classifier. We chose to use the KNN
classifier as it is characterized by simplicity and speed
which are very important for our work. On the other
hand, Gu proved in [13] that SVDA is adapted with
KNN and can improve widely its classification task.
Pose estimation: -19
Pose
estimation
Angle θ
Image
4. Experimental Results
if
θ = 75°
∑θ
i =1
Y
θ > 67.5°
θi
6
θ є ]52.5,67.5]
θ = 60°
θ є ]37.5,52.5]
θ є ]22.5,37.5]
θ = 45°
θ = 30°
θ є ]7.5,22.5]
θ = 15°
In this Section, the experimental results of the
proposed method on UMIST face database are
described. UMIST face database consists of 550 face
images of 20 distinct persons/ subjects. Faces in the
database cover a wide range of poses from profile
(90°) to frontal (~0°) view. The database covers also a
mixed range of race, gender and appearance, such as
different expressions, illuminations, glasses/no
glasses, beard/no beard, different hair style, etc.
To prove the efficiency of inter-communication of
classifiers, we compare the proposed system with a
traditional system using LST (69 low frequency)
θ ˂ 7.5°
θ = 0°
Figure 3. The strategy for quantification of image angles.
Figure 4 summarizes the general operation of the
approach. Suppose for example that the angle in a test
image is θ = 20°:
• We calculate the values θi to specify the priority of
classifiers, as follows:
Pose estimation: 20
Pose
estimation
Angle θ
Pose
estimation
Test image
Test
signature
Quantification
(Figure 3)
Training database
LST + SVDA
Training
signatures
Six weighted KNN
classifiers
(expression 10)
75°
60°
0°
15°
.
.
.
.
.
.
.
.
.
.
.
.
45°
30°
75°
0°
Quantified angles
majority vote
Final result
Figure 4. The general operation of proposed approach.
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The International Arab Journal of Information Technology, Vol. 11, No. 4, July 2014
Error rate(%)
followed by SVDA and a single KNN classifier. Test
conditions are not ideal: each person is presented with
six images (one image per view class) and the
particular case of cross-validation, leave-one-out, is
used as a testing technique. In leave-one-out, the
algorithms run N times. In each round, N-1 samples are
used for training and the remaining sample is used for
testing. If the tested sample is correctly predicted, the
test accuracy of the round is 100%, otherwise it is 0%.
The test accuracy of the leave-one-out strategy is the
mean accuracy of all the N predictions. Which means
that in our case, in each round, the view class of the test
image is not present in the training database, which
presents a complex test situation.
As shown in Figure 5, between 1 and 4 SVDA
eigenvectors the results are similar, it is evident that the
information is still insufficient. Between 4 and 10
eigenvectors we can notice a small improvement in our
system, and for 10 eigenvectors and more the efficiency
of our classification technique is clear. We found that
the inter-communication between classifiers increases
the robustness of the system by 4.17%.
identified the true person, so after the majority vote
they won the vote with a weight of 2.6567 (0.97015 +
0.95522 + 0.73134) versus the other classifiers, 45°,
30°, and 0°, that have returned the same wrong
identity with a weight of 2.34329 (0.8806 + 0.80597 +
0.65672). Therefore, the person is correctly identified.
Total weights: 2.6567
Sought identity
Test image
Pose estimation: 69
Aligned image
a) Pose estimation and the final result after the majority vote.
Weight: 0.97015
Classifier 75°
Weight: 0.95522
Classifier 60°
Weight: 0.8806
Classifier 45°
Weight: 0.80597
Classifier 30°
Weight: 0.73134
Classifier 15°
Weight: 0.65672
Classifier 0°
b) Result of each classifier and the weights used in the majority vote.
b) Result of each classifier and the weights used in the majority vote.
Figure 7. Test using the proposed system.
SVDA Egenvectors
Figure 5. Comparison between the proposed system and a
traditional system.
Figure 6 shows a sample test with a profile view
using the traditional system (without intercommunication of classifiers). A single classifier is
used for the classification, LST (69 low frequency)
followed by SVDA (14 eigenvectors) are used for
feature selection, and test conditions are non-ideal. This
system did not give the correct results.
Test image
Results image
Wrong result
True result
Figure 6. Test using traditional system.
Figure 7 shows the operation of an authentication by
our system using the same profile view in Figure 6.
Classifier 75° has the highest priority because it is the
closest to the estimated angle which equals 69°,
therefore it has the largest weight: 0.97015 (calculated
using Expression 10). Classifiers 75°, 60° and 15° have
It is to be noted that it is possible to use only the
classifiers close to the view class, to minimize the
complexity. But this trick will work only if the
conditions are ideal and it can cause considerable
degradation of system performance when used for
example in the test conditions similar to those
presented in Figure 5. Our experience has shown that
the use of six classifiers in the majority vote is needed,
especially in non-ideal test conditions.
To validate the experimental results obtained by the
proposed system, we have presented in Table 1 a
comparison with several works on multi-view face
recognition using similar test conditions. We divided
UMIST database into two subsets: the training set of
200 images, such as 10 images per person are
carefully selected according to the head pose, and the
test set contains the remaining 350 images. The size of
each cropped image in all the experiments is 92×112
pixels. Thus, each image can be represented by a
10304-dimensional vector in image space. Each face
image vector was normalized to unit before use (to
have unit variance). We reduced the input
dimensionality (originally at 10304) by projecting the
data onto its 69 LST low frequency coefficients. The
final transformation matrix contains 14 SVDA
eigenvectors corresponding to the largest 14
eigenvalues.
Inter-Communication Classification for Multi-View Face Recognition
In these test conditions, we have achieved a
recognition rate of 100%. Former works on multi-view
face recognition have been occupied either by
developing feature selection techniques to minimize
error rate or by developing classification techniques to
minimize the runtime. Consequently, the balance
between recognition rate and running time was never
achieved in these works. Contrariwise, results of
Table1 show an increase in recognition performance
with our system over previous works, keeping a very
acceptable runtime.
Table 1. Comparison between our approach and some other works.
Criteria
Test Base
Recognition
Rate
Runtime
KDDA [21]
UMIST
94.3%
---
SVM-RBF [15]
UMIST
96.7%
---
UMIST
96.7%
(Linear kernel)
98.2%
(RBF kernel)
Technique
SVM-DFS [7]
463 ms
4.16 s
M -SVM [8]
UMIST
93.1%
1.65 s
Proposed
UMIST
100%
3.32 s
It should to be noted that incorrect pose estimation
may influence the recognition performance of the
proposed system, because the weights of classifiers will
be miscalculated. Therefore, the majority vote may
promote a wrong result. Incorrect pose estimation takes
place, mainly, if the detection of face is inaccurate. In
the case of UMIST database, all face images are
cropped and well detected, which means that all pose
estimations are relatively correct.
5. Conclusions
In this paper, we proposed a new system of multi-view
face recognition. For the features selection, we
proposed the Laplacian Smoothing Transform (LST) to
extract the low frequency smooth features of an image
and high frequencies (containing noise) can be removed
while keeping the dependency between neighboring
pixels. We proposed to follow LST by Discriminant
Analysis via Support Vectors (SVDA), the new multiclass approach, to reduce the dimensionality. We used
information provided by the model of the automatic
pose estimation to propose a technique of intercommunication between multiple KNN classifiers, to
improve the efficiency and robustness of the system.
This work can be considered as a stepping stone for
future work in this research direction. The future works
are suggested to optimize the pose estimation model to
minimize the execution time and use other larger
databases to validate the performance of the proposed
system. We are also planning to propose and test
various other techniques of weighted majority vote and
make a non-linear SVDA. Then, for further work, we
will focus on other areas of research related to an
important complement of biometrics: Biometric
security protection.
393
Acknowledgements
This work was supported by the Research Center of
College of Computer and Information Sciences, King
Saud University. The authors are grateful for this
support.
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Inter-Communication Classification for Multi-View Face Recognition
Chouaib Moujahdi is a PhD student
in Engineering Sciences Department
at Mohammed V-Agdal University.
He received his Master’s in 2010
from the same University. He had a
Fulbright Joint Supervision award to
visit the University of Nevada - Reno
for the period between 2012 and 2013. His research
interests include biometrics and pattern recognition. His
current work focuses on biometric security protection.
Sanaa Ghouzali received her
Master's and the Ph.D. degrees in
computer
science
and
telecommunications from Mohamed
V-Agdal University in 2004 and
2009, respectively. She
was a
Fulbright visiting student at Cornell
University between 2005 and 2007. She was an
assistant professor at ENSA (the National school of
Applied Sciences), within the University Abdelmalek
Essaadi, between 2009 and 2011. In 2012, she joined
the College of Computer and Information Sciences at
King Saud University where she is an assistant
professor in the Department of Information
Technology. Her research interests include statistical
pattern detection and recognition, biometrics, biometric
security and protection.
Mounia Mikram is a an assistant professor of
computer sciences and mathematics at the School of
Information Sciences, Rabat since 2010. She received
her master degree from Mohammed V University Rabat
(2003) and her PhD degree from Mohammed V
University, Rabat, and Bordeaux I University (2008).
Her research interests include pattern recognition,
computer vision, and biometrics security systems.
395
Abdul Wadood received his BE
degree from COMSATS Institute of
Information Technology, Islamabad,
Pakistan, in 2004. He did Masters
from University of Limoges, France
in 2007, and PhD in signal and
image processing from University
of Poitiers, Poitiers, France in 2011. Currently, he is
working as an assistant professor at the Department of
Computer Engineering, CCIS, King Saud University,
Riyadh, Saudi Arabia. His research interests are
focused on color image watermarking, steganography,
fingerprinting, and biometric template protection.
Mohammed Rziza Received his
national Doctorate in engineering
sciences,
image
processing
specialty, from the Faculty of
Science of the Mohammed V-Agdal
University, Rabat, Morocco, in
2002. He joined the Faculty of
Science, Rabat, Morocco, in 2003, as an assistant
professor. Since 1997, he is a member of the GSCM
group. His research interests include image
processing, pattern recognition, and stereovision.