Pattern Analysis and Applications

, Volume 11, Issue 3–4, pp 373–383 | Cite as

Binarized eigenphases applied to limited memory face recognition systems

Theoretical Advances

Abstract

Most of the algorithms proposed for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited memory. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique increases the face recognition rate and at the same time achieves substantial savings in the computational time, when compared to other known systems. Experiments on two independent databases of face images are reported to demonstrate the effectiveness of the proposed technique.

Keywords

Face recognition Limited memory PCA MPEG-7 

1 Introduction

In the last few years, researchers in the area of face recognition have proposed many numerous techniques that achieve high recognition rate [1, 2, 3, 4, 5, 6, 22, 23, 24]. Despite the significant advances in face recognition approaches, it has yet to achieve levels of practicality required for many commercial and industrial applications. With the increased commercial interest in portable devices and with the advanced real-time face recognition systems, the need for more practical, cost effective and low-power implementation of these systems has increased. The typical face recognition algorithms that found great applications in different fields (banks and airports) may not be executable in devices that are memory-constrained, with computer processing unit (CPU) speed of no more than 100–400 MHz. With this low processor speed many face recognition approaches will face many difficult challenges. Further, the limited memory of these CPUs put more burdens on the practical implementations. Thus, this important implementation issue for these devices needs special treatment and more investigation. Moreover, to the best knowledge of the authors, there are few published works that propose schemes for devices of limited memory constraints [7, 8, 9]. Lee et al. [7] presented face authentication algorithm that uses the features chosen by Genetic algorithms as an input vector to a support vector machine. Ng et al. [8] proposed another algorithm that copes the illumination and pose variations using boosting algorithm to determine which pose variation of the face are challenging and to bootstrap them into filter synthesis. One of the main challenges of face recognition algorithms is the considerable amount of calculations and computations involved in the process, and eventually this will slow down the system significantly. For example, in surveillance systems face recognition plays an important role for reliable security issues. The wireless domain for data transmission and the low-memory requirement of the data sent are of great important interests.

Most of the appearance-based methods for face recognition that deal with the face images as a whole, depend on calculating the eigenvalues and the eigenvectors of the covariance matrix of a system representing this face space [10, 11, 12, 13, 14]. The time required for these calculations is relatively huge. Consequently, for low-memory devices, we cannot just apply the principal component analysis (PCA) or even other known face recognition methods such as linear discriminant analysis (LDA) to the face images directly.

In this paper, we describe a new approach for face recognition system that can be implemented and loaded on small and portable devices. The proposed approach provides an extension to the well-known MPEG-7 Fourier Feature Descriptor (FFD) algorithm. The main aim beyond this process is to dramatically reduce the size of the space needed to represent the face features, while keeping the performance rate as high as possible. We propose a novel technique for face recognition for systems with a limited memory.

It will be shown that the new technique provides improvements of the performance of the face recognition rates when compared to the MPEG-7 FFD vector method, in particular, as well as for other approaches such as the fisherface LDA method, the direct eigenphase implementation method to the face space [15], and the PCA method.

This work is organized as follows. A brief description of MPEG-7 FFD is given in Sect. 2. The formulation of the proposed technique is presented and discussed in Sect. 3. In Sect. 4, results of testing and implementing the new technique on two independent databases, the Olivetti Research Ltd. (ORL) database and xm2vts, CVSSP—University of Surrey database, are presented. Concluding remarks are given in Sect. 5.

2 MPEG-7 Fourier feature descriptor

The MPEG committee is primarily known for the successful development of a series of video compression standards: MPEG-1, MPEG-2, and MPEG-4. The MPEG-7, formally named “Multimedia content description interface”, objective is to describe the content of multimedia data so that it can be efficiently searched, accessed, transformed or adapted for use by any device and to support different applications [16].

In addition, many face descriptors for MPEG-7 have been proposed for face retrieval in video streams. MPEG-7 is very flexible where improved algorithms can replace previous ones and therefore, not frozen in time.

MPEG-7 uses the FFD vector to represent the facial feature of an image. The descriptor represents the projection of a face vector onto a set of basis vectors, which span the space of possible face vectors. The face recognition feature set is extracted from normalized face images each of size 56 × 46. The FFD vector represents the facial feature of an image using a small single vector. This small vector is derived from two feature vectors; one is a Fourier spectrum vector x1f, and the other is a Multi-block Fourier amplitude vector x2f of a normalized face image.

Figure 1 shows the process of extracting the FFD for a single image. The quantized elements representing the FFD vector, Wf is of size 63. From Fig. 1, we see that the normalized image of size 56 × 46 goes through parallel processing or stages: (1) the image is taken as a whole and its Fourier spectrum vector x1f is found. This vector describes the image globally; (2) the image is divided into different blocks and the Multi-block Fourier amplitude vector x2f is found. This x2f vector describes the image locally; (3) the x1f and x2f vectors are projected using Linear Discriminant Analysis using Principal Component (PCLDA) to find the normalized vectors y1f and y2f; (4) the vectors y1f and y2f are joined to form one vector where this vector is projected using Linear Discriminant Analysis (LDA); (5) finally this resultant vector is quantized to produce a small, efficient, and descriptive vector Wf of size 63. Now, the FFD vectors of all images in the system will span a new space and of course, the elements of the vectors are of different weight of importance. The FFD vectors will form the basis of the MPEG-7 face recognition algorithm. More detailed description of the MPEG-7 FFD-based face recognition can be found in [17, 18].
Fig. 1

The process of Fourier feature extraction for a single image in the MPEG-7 algorithm; Re [F] and Im [F] denotes the real and imaginary parts of the Fourier Transform of the image, and PCLDA refers to the Linear Discriminant Analysis using Principal Component

At this point, it is very beneficial to investigate and find out how these important elements or features of the FFD vectors are distributed. Accordingly, one can emphasize the important features and neglect the relatively less important ones. In this process, we are proposing to achieve this objective by transforming these vectors into another domain, namely the frequency domain. Constructing the face recognition system from the FFD vectors in frequency domain is explained next in Sect. 3.

3 Binarized eigenphases of the MPEG-7 FFD vectors

Given a system of M face images of size N × N each (called face space), the calculation of the eigenvalues and the eigenvectors is the major process in most of the appearance-based methods for face recognition. This calculation takes the major part of the processing time and the memory of the system. MPEG-7 FFD algorithm provides a solution to these limitations by avoiding the calculations of the eigenvalues and the eigenvectors altogether and by reducing the face space of the system to 63 × M vectors instead of × N × M. This explains the fast processing of the MPEG-7 algorithm. Following this great important advantage of MPEG-7, we propose a novel algorithm to enhance further the recognition rate and the memory requirement of the face recognition system. This is achieved by the system shown in Fig. 2.
Fig. 2

Block diagram for the new system

In this figure, it can be seen that the face space is not represented directly by the images of the database in use (as it is done usually); rather it is represented by the Fourier transform of the covariance matrix constructed from the MPEG-7 FFD vectors of these images as a first step. Note that the MPEG-7 FFD vectors Wf1,Wf2,…, WfM are found for the images, where M is the number of images in the database. Then, the average FFD vector of the Wf1,Wf2,…, WfM, vectors is calculated
$$ m = \frac{1}{M}\sum\limits_{{i = 1}}^{M} {W_{{fi}} } $$
(1)
Next, the average vector, m, is used to construct the covariance matrix Cs as:
$$ C_{s} = AA^{T} $$
(2)
where = [D1D2……DM], and Di = Wfi − m. Equation 2 can be rewritten as
$$ C_{s} = \sum\limits_{{i = 1}}^{M} {(W_{{fi}} - m)(W_{{fi}} - m)^{T} } . $$
(3)

Note that Eq. 3 represents the covariance matrix in the spatial domain and its size is reduced to 63 × 63. Oppenheim et al. [19] showed that the phase angle of the Fourier transform retains most of the information about the image. In addition, recently, Savvides et al. [15] proposed a work based on this frequency domain concept. In their work, they analytically demonstrated that the eigenvectors of a face space can be obtained in frequency domain and these frequency domain vectors are equivalent to the ones obtained in the space domain using the principal component analysis. Further, as the phase information retains the most of the intelligibility features of an image, the image variation was modelled by only keeping the complex phase spectrum of the image. Note that, in general, changes in the images affect the magnitude more than the phase. This effect can be reduced by eliminating the magnitude and using only the phase. Now, since much of the noise, distribution, distortion, and image corruption are noticeable in the magnitude part of the spectrum of the image, so by taking off the magnitude we are actually reducing the weaknesses of the image and keeping only the discriminative features presented in the phase part of the Fourier transform. Furthermore, the magnitudes of the spectra of the signals tend to fall off at higher frequencies and many recognizable characteristics of the images will be at these higher frequencies. Thus, in this regard, the eigenphase approach [15] was very successful and very tolerant to illumination variations in the images. Following this approach, it is expected that our proposed binarized eigenphase scheme will outperform the FFD approach.

Accordingly, we will represent our system with the phases of the Fourier transform of the covariance matrix constructed from the FFD vectors. The phase component acts to position the bright and dark spots in the image in order to form regions that are recognizable by a viewer. Thus, the phase component retains the most important information about the system of FFD vectors in the covariance matrix. Following reference [15] analysis, and if we denote Cf as the covariance matrix in the frequency domain, then Cf is given by
$$ C_{f} = F_{{{\text{DFT}}}} C_{s} F_{{{\text{DFT}}}}^{{ - 1}} $$
(4)
where FDFT is the Fourier transform matrix containing the Fourier basis vectors. Cf is a complex-valued matrix whose elements are represented by magnitude and phase. Finding the frequency domain of a system should provide us with the frequency contents of the system under study, where some of these components are more important than others.
As the angles of the elements of Cf retain most of the information of the images, the phases Φ(i, j) of the elements of Cf are extracted and the magnitudes are ignored. On the other hand, it is well known in digital image processing that some important features of an image can be emphasized through enhancing the high frequency components of that image. We can look at our system as a matrix that has some elements of high frequency and others of low frequency. The enhancing process of these high frequency components can be done by sharpening the system. This operation can be done by a binarization process where the phase Φ(i, j) of each element of Cf is replaced by a binary value (either 1 or 0) according to:
$$ B\Upphi (i,j) = \left\{ \begin{gathered} 1\quad \quad for\;\Upphi (i,j) \ge T_{H} \hfill \\ 0\quad \quad otherwise \hfill \\ \end{gathered} \right. $$
(5)
Here TH is a threshold value that is used for binarization. The threshold value has been set to the median of the phases of the 3 × 3 neighborhood elements surrounding the targeted phase element. More specifically, we will find the following value:
$$ \begin{gathered} T_{H} = {\text{Median}}\;\{ \Upphi (i,j),\;\Upphi (i + 1,j),\;\Upphi (i - 1,j),\;\Upphi (i,j + 1),\;\Upphi (i,j - 1),\; \hfill \\ \Upphi (i + 1,j + 1),\;\Upphi (i - 1,j - 1),\;\Upphi (i + 1,j - 1),\;\Upphi (i - 1,j + 1)\} \hfill \\ \end{gathered} $$
(6)
then we compare this value with Φ (i, j) to see if it is larger or smaller, and consequently we assign a value of 1 or 0 to the corresponding phase element BΦ (i, j). The reason behind choosing a 3 × 3 size is that it is one of the smallest filters that we can apply since we are looking for the minimum number of computations and operations. This binarization step will help in locating the features of interest (the phases that contribute most of the discriminative information about the system). It is similar to provide the silhouette of the object when finding the binary of an image. By applying this step, we gain three important advantages:
  1. Low memory storage: no more than 1 bit/pixel, instead of representing the pixel with 8 bits is used.

     
  2. Simple processing: the algorithms are in most cases much simpler than those applied to grey-level images are.

     
  3. Enhancing the performance of the system, as will be seen in Sect. 4.

     

A further significant improvement of the proposed system is obtained by reducing the dimensionality of the system in order to cope with the system constraints and requirement. Since each feature adds to the computational burden in terms of processing and storage, the application of the PCA at the final step further reduces the dimensionality of our system.

A value of M′ (M′ is much smaller than M) eigenvectors associated with the largest eigenvalues is sufficient for the recognition process. It is found from the experiments that M’′ = 10.

Now, the FFD vector (Wf) is transformed into its eigenvector components by the following weight equation
$$ \omega _{k} = u_{k}^{T} (W_{f} - m) $$
(7)
where ui is the ith eigenvector and k = 1, …., M. The weights form a vector \( \Omega ^{T} = [\omega _{1} ,\omega _{2} , \ldots \ldots ,\omega _{{M'}} ] \) that describes the contribution of each eigenvector in representing the input binarized phase FFD vector, and consequently the ΩT represents the corresponding (original) face image.
We have used the Euclidian distance to determine which face class provides the best description of an input face image by finding the face class k that minimizes the Euclidian distance
$$ \varepsilon _{k} = \left\| {(\Omega - \Omega _{k} )} \right\|^{2} $$
(8)
where Ωk is a vector describing the kth binarized phase FFD of a face class and it is calculated by averaging the results of the eigenvector representation over a small number of binarized phase FFD vectors of each individual. A face is classified as belonging to class k if the corresponding εk is the minimum among all other εk‘s.

4 Experiments

Many different experiments were performed to evaluate the performance of the proposed system. We carried out experiments on two independent and different databases, one is the ORL and the other is the xm2vts. Both sets include a number of images for each person, with variations in pose, expression and lighting. The ORL set includes 400 images of 40 different individuals where each individual is represented by 10. The system was trained using five images for each person from this set and tested using the other five images.

For the xm2vts set, we have used 2,360 images for 295 different individuals with each individual represented by 8 different images. These images have been taken at four different sessions, with two shots at each session. The xm2vts uses a standard protocol, referred to as Lausanne protocol [20]. This protocol was defined for the task of verification. The features of the observed person are compared with stored features corresponding to claimed identity, and the system decides whether the identity claim is true or false on the basis of a similarity score. The subjects, whose features are stored in the system database, are called clients, whereas the person who is claiming a false identity is called imposter. According to Lausanne protocol, the database is split into three groups: the training group, evaluation group, and the testing group. We have trained our system with the images from the first two sessions (4 images), and used the images from the third session for evaluation (2 images), and finally we have used the images from the fourth session for testing (2 images). The evaluation set is used to find the threshold that determines if a person is accepted or rejected. The xm2vts database images are taken at different sessions (different days). The experiments on this database test the robustness of the proposed system under the variation in time conditions of the images. Different timing means different hairstyle, different clothes and different “moods”.

Now, in order to draw solid conclusions and to show the robustness of our system, we have implemented three stages of experiments to the xm2vts database. At the first stage, we have used 1,180 images that were taken on shot 1. Second stage uses the other 1,180 images that were taken on shot 2. For stages 1 and 2, we have trained our system using 2 images per person, used one image for evaluation, and used one image for testing. At the final stage, we have utilized the whole 2,360 images for the 295 individuals where we have trained our system with 4 images from the first two sessions, and used the 2 images from the third session for evaluation, and finally we have used the 2 images from the fourth session for testing. Note that the evaluation set is used to find the threshold that determines if a person is accepted or rejected.

Figure 3 shows examples of both the xm2vts and the ORL databases before normalization. Note that an example of the actual images that were used (of size 56 × 46 after normalization) is shown in Fig. 4. The images of Fig. 4 represent low resolution ones, a situation that is very likely to be expected in the case of having mobile phones with low resolution cameras and low memory limitations.
Fig. 3

Examples from a the xm2vts database and b the ORL database

Fig. 4

Examples from the xm2vts database images that are used in the experiments after normalization to a size of 56 × 46

The following steps were carried out on both database sets:

  1. 1.

    The images were normalized to a size of 56 × 46.

     
  2. 2.

    The MPEG-7 algorithm was applied to these sets, and the FFD for each one of the images was calculated.

     
  3. 3.

    The eigenvectors and eigenvalues of the (63 × 63) covariance matrix were calculated, the M′ eigenvectors corresponding to the highest associated eigenvalues is chosen. M′ = 10 was selected in the experiments.

     
  4. 4.

    For each known individual, the class vector Ωk was calculated by averaging the pattern (weight) vectors Ω for the learning images calculated from the original FFD vector of each individual.

     
  5. 5.

    For each new face image to be identified, its pattern vector Ω was found and the distances ɛk to each known class was calculated. The class vector Ωk that has the minimum distance ɛk will represent this input face.

     

The new system achieved 93.5% recognition rate when applied to xm2vts database, while under the same conditions, the MPEG-7 face recognition method achieved 89.5%.

The other experiment was to test the proposed technique under other different circumstances. The ORL face database is used in this experiment. This database include images with different poses, different illuminations, different expressions (open or closed eyes, smiling or non-smiling), different facial details (glasses or no glasses), and some of them were taken at different times. Examples of the ORL database used are shown in Fig. 3b. The proposed technique achieved 95.5% correct classification, while under the same conditions the MPEG-7 face recognition method achieved 91.5% correct classification.

A summary of the results of the recognition test on both databases is given in Table 1. Note that in this table, the results of applying the PCA method, Fisherface-LDA method, and the direct eigenphase implementation method to the face images are also shown.
Table 1

The recognition rates of five different methods

 

PCA (%)

Eigenphase (%)

Fisherface-LDA (%)

MPEG-7 (%)

New method (%)

ORL

78

81

90

91.5

95.5

xm2vts

75

79

86

89.5

93.5

It is significant to emphasize at this point the importance of the binarization step in the new proposed method. The performance of the system has degraded by ~2% for both databases without the binarization step. This shows the importance of the binarization step in enhancing the performance of the system in addition to the memory saving of the system.

To prove the efficiency of our system, we have applied an evaluation methodology proposed by the developers of FERET [21] where the performance statistics are reported as cumulative match scores. In this case, identification is regarded as correct if the true object is in the top Rank n matches. The results of the evaluation for the ORL, xm2vts shot 1 images, xm2vts shot 2 images, and xm2vts with both shots images databases are shown in Figs. 5, 6, 7, and 8, respectively.
Fig. 5

The cumulative match scores of the five methods for the ORL database

Fig. 6

The cumulative match scores of the five methods for the xm2vts shot 1 database

Fig. 7

The cumulative match scores of the five methods for the xm2vts shot 2 database

Fig. 8

The cumulative match scores of the five methods for the xm2vts database taking both shots (2,360 images)

We have also assessed the performance of our new technique by comparing between the methods using the receiver operating characteristic (ROC) curve. The ROC curves for the ORL, xm2vts shot 1 images, xm2vts shot 2 images, and xm2vts with both shots images databases are shown in Figs. 9, 10, 11, and 12, respectively. It is clear from the figures that our proposed method outperforms the other methods.
Fig. 9

The ROC curves of the five methods for the ORL database

Fig. 10

The ROC curves of the five methods for the xm2vts shot 1 database

Fig. 11

The ROC curves of the five methods for the xm2vts shot 2 database

Fig. 12

The ROC curves of the five methods for the xm2vts database taking both shots (2,360 images)

Further, the proposed new technique is tested on low-resolution images as well as noisy images. Having a very low quality and being not clean images, the proposed technique is tested under more resembling conditions to actual ones. Examples of such images are shown in Figs. 13 and 14. Figure 13 shows images that were blurred using 3 × 3 filter. This filter gives image of low-resolution and low quality. Figure 14 shows example of images affected by Gaussian noise, where the images have become low quality and not clean.
Fig. 13

Examples from the xm2vts database images under the effect of 3 × 3 blurring filter

Fig. 14

Examples from the xm2vts database images that are used in the experiments after applying Gaussian noise

The performance of the five different methods using the blurred images of ORL and xm2vts (with the whole 2,360 images) databases are shown in Figs. 15 and 16, respectively. The Guassian noise effect on the performances of the proposed system and the other recognition methods for ORL and xm2vts (with the whole 2,360 images) databases are shown in Figs. 17 and 18, respectively.
Fig. 15

Performance of the five different methods for the ORL database for blurred images

Fig. 16

Performance of the five different methods for the xm2vts (2,360 images) database for blurred images

Fig. 17

Performance of the five different methods for the ORL database for noisy images

Fig. 18

Performance of the five different methods for the xm2vts (2,360 images) database for noisy images

From Figs. 15, 16, 17, and 18, one can see that the performances of all five methods are degraded when they are applied to more practical images. However, our proposed technique still outperforms the other ones as it is expected.

One of the big advantages of our proposed method is obviously its time saving of the computations. A closer look at the computation processes of the face recognition methods (except of MPEG-7 method) reveals that the huge size of the covariance matrix, which undergoes the process of finding the eigenvectors and eigenvalues, is the limiting factor.

In the PCA, LDA, and the eigenphase methods, the program spent most of the time in calculating the huge size covariance matrix. The numbers of multiplications and summations needed to construct the covariance matrix can be approximated as M × M × N2 and M × M × (N2 − 1), respectively, where M is the number of images needed to build the covariance matrix and × N is the image size (assume a square image). Whereas for our new method, these numbers are 63 × 63 × M and 63 × 63 × (– 1). For example, if the image size is × N = 32 × 32 and the number of images M = 100, then for this example, the number of multiplications and summations needed to construct the covariance matrix using the PCA, the LDA, or the eigenphase methods is of order O (107). On the other hand, this number is of order O (105) for the new method. Note that the MPEG-7 method does not find the covariance matrix and the corresponding eigenvectors at the same level, and consequently the computational complexity cannot be compared to the previous methods.

Table 2 demonstrates the amount of time consumed by each method to perform the recognition process. In Table 2, τ is the time taken by the machine to perform the recognition process for a specific example (image size × N = 32 × 32 and the number of images M used for this demonstration is 100 images). Table 2 illustrates clearly the significant reduction in time for our technique (an approximate ratio of more than 1:3,000 is achieved when compared to the PCA method). So, if it takes a Pentium 4 machine with 2.8 GHz processor that much time, we can imagine how costly it will be for small memory devices that have a speed of only 100–400 MHz. Note that the MPEG-7 method also provides good time saving, but the recognition performance is not as good when compared to our method (see Table 1). The results shown in Tables 1 and 2 clearly demonstrate that the proposed method makes the application of face recognition more practical for systems requiring low memory and high speed. To stress further the importance of this issue, note that while we are keeping the system busy in processing only 100 images using PCA, LDA, or eigenphase method, we can process more than 300,000 images in the same amount of time using our new method. This shows that our method does not require the system to allocate large amount of hard disk memory or reserve some virtual memory to accommodate the complicated computational processing requirements. In other word, we can utilize the limited memory of the system to accomplish other tasks instead of holding it busy in processing only one task. Moreover, while grey level images require 8 bits of memory for representation, as in case of PCA, LDA, or eigenphase, and 5 bits in case of MPEG-7 method [17], our technique does not require more than 1 bit for representation when dealing with binary vectors.
Table 2

Summary of calculations and consumed time for a certain example

 

PCA

Eigenphase method

LDA

MPEG-7

New method

τa = time

37 s

42 s

40.5 s

0.009 s

0.012 s

aUsing Pentium 4, 2.8 GHz, 512 M RAM

5 Concluding remarks

In this paper, a novel method is proposed for efficient face recognition that can be implemented in systems that have limited memory capabilities and have low speed processors. Due to the recent fast advances in technology, face recognition techniques that utilize small memory size devices and show robustness in performance are worth more investigation. Although the appearance-based methods (such as the PCA and the fisherface) have been proposed for face recognition tasks, they are overwhelmed by the significant amount of time taken to calculate the eigenvectors of the covariance matrix; a problem that we have overcome in our new technique. The new technique exploits the characteristics and combines the advantages of the MPEG-7 FFD vectors, frequency domain, binarization process, and the PCA. The MPEG-7 FFD reduces the huge dimensionality of the system and provides compact and small memory size vectors for further processing. The frequency domain provides the phase contents of the images where the intelligent features are residing. The binarization process emphasizes the important features of the covariance system through enhancing the high frequency components. Finally, the PCA is applied to the system for further dimensionality reduction. As the covariance matrix of the face space in our system is represented in an efficient way (63 × 63), the calculations related to the eigenvectors of the covariance matrix are dramatically reduced. Moreover, the binarization step of the new system enhances the performance of the system and provides better recognition rate compared to other known techniques when applied to two independent face images databases.

Notes

Acknowledgments

The authors would like to thank the reviewers for the constructive suggestions and valuable comments.

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Naser Zaeri
    • 1
  • Farzin Mokhtarian
    • 1
  • Abdallah Cherri
    • 2
  1. 1.Electronic and Electrical Engineering DepartmentUniversity of SurreyGuildfordUnited Kingdom
  2. 2.Electrical Engineering DepartmentKuwait UniversitySafatKuwait

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