Research Article  Open Access
Gaoyun An, Shuai Liu, Yi Jin, Qiuqi Ruan, Shan Lu, "Facial Expression Recognition Based on Discriminant Neighborhood Preserving Nonnegative Tensor Factorization and ELM", Mathematical Problems in Engineering, vol. 2014, Article ID 390328, 10 pages, 2014. https://doi.org/10.1155/2014/390328
Facial Expression Recognition Based on Discriminant Neighborhood Preserving Nonnegative Tensor Factorization and ELM
Abstract
A novel facial expression recognition algorithm based on discriminant neighborhood preserving nonnegative tensor factorization (DNPNTF) and extreme learning machine (ELM) is proposed. A discriminant constraint is adopted according to the manifold learning and graph embedding theory. The constraint is useful to exploit the spatial neighborhood structure and the prior defined discriminant properties. The obtained partsbased representations by our algorithm vary smoothly along the geodesics of the data manifold and have good discriminant property. To guarantee the convergence, the project gradient method is used for optimization. Then features extracted by DNPNTF are fed into ELM which is a training method for the single hidden layer feedforward networks (SLFNs). Experimental results on JAFFE database and CohnKanade database demonstrate that our proposed algorithm could extract effective features and have good performance in facial expression recognition.
1. Introduction
Facial expression recognition plays an important role in humancomputer interaction, and 55% information is transferred by facial expression in facetoface human communication [1]. Although many methods were proposed, recognizing facial expression is still challenging due to the complex, variable, and subtle facial expressions.
One of the effective methods for facial expression recognition is the subspacebased algorithm [2–5]. It aims to project the samples into a lower dimensional space which preserves the needed information and discards the redundant information. There are many widely used subspacebased algorithms, for example, principle component analysis (PCA) [2], linear discriminant analysis (LDA) [3], neighborhood preserving embedding (NPE) [4], localitypreserving projection (LPP) [6], and singular value decomposition (SVD) [5], and so forth.
Recently, the nonnegative matrix factorization (NMF) was introduced into facial expression recognition [7]. NMF decomposes the face samples into two nonnegative parts: the basis images and the corresponding weights. As many data in bases and weights were degenerated too close to zero, NMF derived the partsbased sparse representations. For facial expression recognition, the localized subtle features, such as the corners of mouth, upward or downward eyebrows, and change of eyes, are critical for the recognition performance. Since NMF yields the partsbased representations, it outperforms the subspacebased models. To further improve NMF, several variants have been presented by introducing different constraints to the objective function. Li et al. put forward a local NMF (LNMF) by adding a local constraint to the basis images [8], to learn a localized, partsbased representation. Hoyer gave a sparse constraint NMF (SNMF) by incorporating sparseness constraint into both the bases and the weights [9]. Cai et al. developed a graph constraint NMF (GNMF) by adding a graph preserving constraint to the weights [10]. Zafeiriou et al. used the discriminant NMF (DNMF) for frontal face verification [11]. Wang et al. extended NMF to PNMF with a PCA constraint and FNMF with a Fisher constraint [12].
For facial expression recognition, NMF and its variants vectorize the samples before factorization, which may lose the local geometric structures. However, the spatial neighborhood relationships within pixels are critical for image representation, understanding, and recognition [13]. Another drawback of NMF is that it could not generate a unique decomposition result. Welling and Weber developed a positive tensor factorization (PTF) algorithm, which handled images as 2D matrices directly [14]. Shashua and Hazan proposed the nonnegative tensor factorization (NTF) which implemented the factorization in the rankone tensor space [15]. The factorization in the tensor space could preserve the local structures and guarantee the uniqueness of the decomposition.
On the other hand, the choice of classifier plays an important role for recognition. For facial expression recognition, nearest neighbor (NN) and support vector machine (SVM) are the commonly used methods [16]. The sparse representation classifier (SRC) was adopted in [17]. Recently, the extreme learning machine (ELM) was proposed for classification which is a training method for the single hidden layer feedforward networks (SLFNs) [18]. The conventional methods need long time to converge or may lose the generalization property due to overfitting. However, ELM converges fast and provides good generalization performance. For ELM, the input weights and biases are randomly assigned, and the output weights can be simply calculated by the generalized inverse of the hidden layer output matrix. Therefore it converges extremely fast and obtains an excellent generalization capability. Many variants of ELM were proposed for different applications [19–26], including the Kernelbased ELM [21] and the incremental ELM (IELM) [23], which lead to the stateoftheart results in different applications.
In this paper, we propose a novel facial expression recognition algorithm based on discriminant neighborhood preserving nonnegative tensor factorization (DNPNTF) and ELM. It works well in the rankone tensor space. The simple ELM is adopted to testify its effectiveness for facial expression recognition [18]. Our algorithm is composed of two stages: feature extraction and classification. Firstly, to extract the discriminant features, a neighborhood preserving constraint form of NTF is used. The constraint is derived according to the manifold learning and graph embedding theory [27–29]. Since the columns of the weighting matrix have a onetoone correspondence with the columns of the original sample, the discriminant constraint is added to the weighting matrix. With the neighborhood preserving constraint, the obtained partsbased representations vary smoothly along the geodesics of the data manifold and are more discriminant. Secondly, the discriminant features extracted by DNPNTF are fed into ELM classifier to conduct the recognition task.
The rest of this paper is organized as follows. The mathematical notations are given in Section 2. In Section 3, we give the detailed analysis about DNPNTF and its optimization procedure. ELM is introduced in Section 4, and the experiments are given in Section 5. Finally, the conclusions are drawn in Section 6.
2. Basic Algebra and Notations
In this paper, a tensor is represented as , whose order is . The element of is denoted by , where , .
Definition 1 (inner product and tensor product [30]). The inner product of two tensors is defined as
The tensor product of two tensors and is
Definition 2 (rankone tensor [30]). A thorder tensor could be represented as a tensor product of tensors as
Here is called rankone tensor, and .
Definition 3 (mode product [30]). The mode product of and is where , , .
3. The DNPNTF Algorithm
In this section, we give a detailed description about the proposed DNPNTF algorithm. Instead of converting into vectors, it processes the samples in rankone tensor space. The objective function of NTF is adopted, which could learn the partsbased representation and have the sparse property. To discover the spatial local geometric structure and the discriminant classbased information, a constraint is added in the objective function according to the manifold learning and graph embedding analysis. To guarantee the convergence, the project gradient method is used.
3.1. The Analysis of DNPNTF
Given a image database , it contains sample images . The dimension of each sample is . In NTF, the database is organized as a 3rdorder tensor , which is overlapped by sequentially. The objective function of NTF is where , , and describe the first, second, and third modules of , respectively. Each sample is approximated by
By minimizing (5), the bases and the corresponding weights are conducted. The inner product of and the sample image is calculated to derive the lowdimensional partsbased representation.
To incorporate more properties into NTF, different constraints could be added into the objective function. The constraint form of objective function is where , , . is the constraint function about and is the constraint about . and are the corresponding positive coefficients. To encode the spatial structure and discriminant classbased information into sparse representations, we propose a constraint function according to the manifold learning and graph embedding analysis. In NTF, the columns of the weighting matrix have a onetoone correspondence with the columns of the original image matrix. Therefore, we add the discriminant constraint to , and for is defined as where denote the graphs with different properties and are the corresponding coefficients. By deriving different , the graph embedding model could have different properties, such as the neighborhood preserving property and the discriminant property.
Now, we discuss the selection of . The most commonly used graph is the Laplacian graph, and is calculated in form of the heat kernel function as
Here, measures the similarity between a pair of vertices and has neighborhood preserving property. To further incorporate the classbased discriminant information, we derive a universal penalty graph [27], where the similarity matrix is defined as where represents the nearest pairs of samples between class and the other classes. The purpose of the penalty graph was to separate marginal samples between different classes.
Now, the objective function of constrained NTF becomes
By solving the generalized eigenvalue decomposition problem, the graph embedding criterion in (11) can be calculated as And the final objective function of DNPNTF is where and to make sure (13) should be nonnegative.
3.2. Projected Gradient Method of DNPNTF
The most popular approach to minimize NMF or NTF is the multiplicative update method. However, it cannot ensure the convergence of the constraint forms of NMF or NTF. In this paper, the projected gradient method is used to solve DNPNTF.
The objective function of DNPNTF can be stated as where is a positive constant. The goal of (14) is to find and by solving the following problem:
To find the optimal solution, (15) is divided into three subproblems: first, we fix and and update to arrive at the conditional optimal value of the subminimization problem; second, we fix and and update ; last, we fix and and update . Three functions are defined as , , and . The update rules are defined as Now the task is calculating , , and .
3.2.1. The Calculation of and
Firstly, we discuss the calculation of and . The objective function could be written as
The differential of is
And the partial differential for is where the th element in is 1 and others are 0. That is, and . According to Definition 1, for any order tensors , , there is . Then (19) could be written as
According to (16), the update rule for is
To confirm the nonnegative of , is set to be Then the update function is where represents the th row of , is the matrix Hadamard product, represents the matrix which fixes the first module of , and traversals are the other two modules. It is defined as
Similarly, the update rule of the th element of is where represents the th row of , is the matrix Hadamard product, represents the matrix which fixes the second module of , and traversals are the other two modules. It is defined as
Now, and are calculated.
3.2.2. The Calculation of
Then we discuss the calculation of . The differential of along is
For , the partial differential for is where the th element in is 1 and others are 0. That is, and . Then the partial differential for is
According to (16), the update rule for is
The update step is set as
And the final update rule of is where represents the th row of ; and represent the th row of and , respectively; is Hadamard product, , and is defined as
Now , , and in the objective function are all calculated.
4. Extreme Learning Machine
ELM is proposed by Huang et al. [18] for SLFNs. Unlike the traditional feedforward neural network training methods, such as the gradientdescent method, the standard optimization method, and the leastsquare based method, ELM need not tune the hidden layer of SLFNs which may cause learning complicated and inefficient. It could reach the smallest training error and have better generalization performance. The learning speed of ELM is fast, and the parameters have not to be tuned manually. In our proposed algorithm, the extracted features by DNPNTF are fed into ELM for classification.
Given a training set , where is the input feature vector and is a target vector. ELM with hidden nodes and activation function is modeled as where represents the input weight, which is the th neuron in the hidden layer and the input layer; is the weight vector between the th hidden neuron and the output layer; is the target vector of the th input data. In training step, ELM aims to approximate training samples with zero error, which means . Then there exist , , and satisfying that
Equation (35) can be reformulated compactly as where is called the hidden layer output matrix of the neural network, and the th column of is the th hidden neuron output with respect to inputs . It is proved by Huang et al. [18] that weights and biases need not be adjusted and can be arbitrarily given. Therefore, the output weights could be determined by finding the leastsquare solution as where is the MoorePenrose generalized inverse of matrix . Furthermore, the smallest training error can be obtained by as As analyzed by Huang, ELM could obtain a good generalization performance with a dramatically increased learning speed by solving (39).
5. Experiments
In this section, we apply DNPNTF via ELM to facial expression recognition. We compare DNPNTF with NMF [7], DNMF [11], and NTF [9] and give the experimental results by employing ELM, NN, SVM [16], and SRC [17]. Two facial expression databases are used: the JAFFE database [31] and the CohnKanade database [32]. Raw facial images are cropped according to the position of eyes and normalized to 32 × 32 pixels. Figure 1 shows an example of the original face image and the corresponding cropped image. According to the rankone tensor theory, gray level images are encoded in tensor space.
Since the results of ELM may vary during each different execution, we repeat the execution for 5 times and take the average value as the final result. It is proved by theory analysis and experiments that the classification performance of ELM is affected by the hidden activation function and the number of hidden nodes [23]. However, in this paper we just focus on the application of ELM to facial expression recognition. The activation function used in our algorithm is a simple sigmoidal function. The number of hidden nodes is set to be the same as the number of facial expression classes (e.g., 7 for the JAFFE database and 6 for the CohnKanade database). For SVM, the radial basis function (RBF) is used, which is that is set to be 3 as an empirical value. For SRC, “Homotopy” algorithm is used to solve the minimization of norm constraint.
5.1. Experiments on JAFFE Database
The JAFFE database [31] is an expression database which contains 213 static facial images captured from 10 Japanese females. Each person poses 2 to 4 examples for each of the 6 prototypic expressions (anger, disgust, fear, happiness, sadness, and surprise) plus the natural face. To evaluate the algorithms, we randomly partition all images into 10 groups, with roughly 70 samples in each group. We take any 9 groups for training and calculate the recognition rates with the remaining one. We repeat it for all the 10 possible choices. Finally, the average result over 10 times’ testing was taken.
The average recognition rates of different feature extraction algorithms are shown in Figure 2, where the vertical axis represents the correct recognition rate in percentage and the horizontal axis represents the corresponding dimensions (from 1 to 120). Here, only the NN classifier is used. In the lower range of dimensions, the recognition rates of DNPNTF are similar to other algorithms. This is because DNPNTF extracts the partsbased sparse representations, and only a few features could be generated for recognition in the low range of dimensions. In the higher range of dimensions, DNPNTF outperforms the others. With the increase of the extracted partsbased features, DNPNTF could achieve good recognition performance. Since different constraints were added, the improved versions of NMF, including DNMF and NTF, outperform the conventional NMF.
The top recognition rates of different algorithms with corresponding dimensions are illustrated in Table 1. NMF achieves the highest rate at a low dimension, while DNMF achieves the highest rate at a high dimension. Although more dimensions are needed, DNPNTF achieves the highest recognition rate compared with others. This is because the constraints about manifold structure and discriminant information are considered, which are critical for classification.

Figure 3 shows the basis images obtained on the JAFFE database by NMF, NTF, and DNPNTF. Based on the principle of NMF, the face images are represented by combining multiple basis images with addition only, and the basis images are expected to represent facial parts. In this database, the basis images calculated by NMF are not sparse. NTF and DNPNTF which execute in the tensor space could generate partsbased sparse representations. Since more constraints were adopted, DNPNTF generate sparser basis images which reflect distinct features for recognition.
(a) NMF
(b) NTF
(c) DNPNTF
Then we give the experiments to prove the effectiveness of DNPNTF via ELM. The average recognition rates of DNPNTF with ELM, NN, SVM, and SRC are given in Figure 4, where the vertical axis represents the correct recognition rate in percentage and the horizontal axis represents the corresponding dimensions (from 1 to 120). ELM and SRC achieve better recognition performance compared with NN and SVM, and ELM achieves the highest recognition rate. The top recognition rates with the corresponding dimensions are given in Table 2.

5.2. Experiments on CohnKanade Database
The CohnKanade database [32] consists of a large amount of image sequences starting from natural face and ending with the peak of the corresponding expression. 104 subjects with different ages, genders, and races are instructed to pose a series of 23 facial displays, including the 6 prototypic expressions. In our experiments, for every image sequence, we take 2 to 8 continuous frames near the peak expression as the static samples. We use the face images of all subjects. We partition the subject to 3 exclusive groups, and in each group, for each of the prototypic expression, we select 100 samples; that is, there are 600 samples in each group and the size of the total set is 1800. During the experiment, we adopt the leaveonegroupout strategy and 3fold crossvalidation: each time two groups are taken as training set and the remaining group is left for testing. This procedure is repeated for 3 times.
The average recognition rates of different algorithms on the CohnKanade database are shown in Figure 5, where the vertical axis represents the correct recognition rate in percentage and the horizontal axis represents the corresponding dimensions (from 1 to 120). Here, only the NN classifier is used. Table 3 shows the top recognition rates with the corresponding dimensions. The recognition rates obtained on the CohnKanade database are lower than those obtained on the JAFFE database. It can be explained that the experiments on the CohnKanade database are personindependent, which are more difficult than the persondependent experiments on the JAFFE database. From Figure 5, we can see that the performance of DNPNTF is superior to others with nearly all dimensions. Its recognition rates improve with the increase of dimensions.

Lastly, we give the experiments about different classifiers on the CohnKanade database. The average recognition rates of DNPNTF via ELM, NN, SVM, and SRC are shown in Figure 6, and the top recognition rates are given in Table 4. SVM and SRC achieve better performance compared with NN. ELM achieves the best recognition accuracy among all tested algorithms on almost all dimensions. It means ELM could use the information contained in extracted features better than other classifiers.

6. Conclusions
In this paper, a novel DNPNTF algorithm with the application to facial expression recognition was proposed, which adopts ELM as the classifier. To incorporate the spatial information and the discriminant class information, a discriminant constraint is added to the objective function according to the manifold learning and graph embedding theory. To guarantee the convergence, the project gradient method is used for optimization. Theoretical analysis and experimental results demonstrate that DNPNTF could achieve better performance compared with NTF, NMF, and its variant. Then the discriminant features generated by DNPNTF are fed into ELM to learn an optimal model for recognition. In our experiments, DNPNTF via ELM achieves higher recognition rate compared with NN, SVM, and SRC.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work was supported partly by the National Natural Science Foundation of China (61370127，61472030), the Fundamental Research Funds for the Central Universities (2013JBM020 and 2014JBZ004), and Beijing Higher Education Young Elite Teacher Project (YETP0544).
References
 A. Mehrabian, “Communication without words,” Psychology Today, vol. 2, no. 4, pp. 53–56, 1968. View at: Google Scholar
 A. J. Calder, A. M. Burton, P. Miller, A. W. Young, and S. Akamatsu, “A principal component analysis of facial expressions,” Vision Research, vol. 41, no. 9, pp. 1179–1208, 2001. View at: Publisher Site  Google Scholar
 W. S. Yanmbor, “Analysis of PCAbased and Fisher discriminantbased image recognition algorithms, Computer Science Department, Colorado State University, Fort Collins, Colo, USA, M.S. Thesis,” Tech. Rep. CS00103, 2000. View at: Google Scholar
 X. F. He, D. Cai, S. C. Yan, and H.J. Zhang, “Neighborhood preserving embedding,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), pp. 1208–1213, October 2005. View at: Google Scholar
 M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, June 1991. View at: Google Scholar
 X. F. He and P. Niyogi, “Locality preserving projections,” in Proceedings of the Advances Neural Information Processing Systems, pp. 153–160, 2003. View at: Google Scholar
 D. D. Lee and H. S. Seung, “Learning the parts of objects by nonnegative matrix factorization,” Nature, vol. 401, no. 21, pp. 788–791, 1999. View at: Publisher Site  Google Scholar
 S. Z. Li, X. W. Hou, H. J. Zhang, and Q. S. Cheng, “Learning spatially localized, partsbased representation,” in Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I207–I212, December 2001. View at: Publisher Site  Google Scholar
 P. O. Hoyer, “Nonnegative sparse coding,” in Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565, 2002. View at: Publisher Site  Google Scholar
 D. Cai, X. He, X. Wu, and J. Han, “Nonnegative matrix factorization on manifold,” in Proceedings of the 8th IEEE International Conference on Data Mining (ICDM '08), pp. 63–72, Pisa, Italy, December 2008. View at: Publisher Site  Google Scholar
 S. Zafeiriou, A. Tefas, I. Buciu, and I. Pitas, “Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification,” IEEE Transactions on Neural Networks, vol. 17, no. 3, pp. 683–695, 2006. View at: Publisher Site  Google Scholar
 Y. Wang, Y. Jia, C. Hu, and M. Turk, “Nonnegative matrix factorization framework for face recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 19, no. 4, pp. 495–511, 2005. View at: Publisher Site  Google Scholar
 T. Hazan, S. Polak, and A. Shashua, “Sparse image coding using a 3D nonnegative tensor factorization,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), vol. 1, pp. 50–57, October 2005. View at: Publisher Site  Google Scholar
 M. Welling and M. Weber, “Positive tensor factorization,” Pattern Recognition Letters, vol. 22, no. 12, pp. 1255–1261, 2001. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 A. Shashua and T. Hazan, “Nonnegative tensor factorization with applications to statistics and computer vision,” in Proceedings of the 22nd International Conference on Machine Learning (ICML '05), pp. 793–800, Bonn, Germany, August 2005. View at: Google Scholar
 C. Cortes and V. Vapnik, “Supportvector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009. View at: Publisher Site  Google Scholar
 G.B. Huang, Q.Y. Zhu, and C.K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at: Publisher Site  Google Scholar
 S.J. Wang, H.L. Chen, W.J. Yan, Y.H. Chen, and X. Fu, “Face recognition and microexpression recognition based on discriminant tensor subspace analysis plus extreme learning machine,” Neural Processing Letters, vol. 39, no. 1, pp. 25–43, 2014. View at: Publisher Site  Google Scholar
 X. Chen, W. Liu, J. Lai, Z. Li, and C. Lu, “Face recognition via local preserving average neighborhood margin maximization and extreme learning machine,” Soft Computing, vol. 16, no. 9, pp. 1515–1523, 2012. View at: Publisher Site  Google Scholar
 G. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 42, no. 2, pp. 513–529, 2012. View at: Publisher Site  Google Scholar
 G. Huang, “An insight into extreme learning machines: random neurons, random features and kernels,” Cognitive Computation, 2014. View at: Publisher Site  Google Scholar
 G.B. Huang and L. Chen, “Enhanced random search based incremental extreme learning machine,” Neurocomputing, vol. 71, no. 16–18, pp. 3460–3468, 2008. View at: Publisher Site  Google Scholar
 J. Cao, T. Chen, and J. Fan, “Fast online learning algorithm for landmark recognition based on BoW framework,” in Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, 2014. View at: Google Scholar
 J. Cao and L. Xiong, “Protein sequence classification with improved extreme learning machine algorithms,” BioMed Research International, vol. 2014, Article ID 103054, 12 pages, 2014. View at: Publisher Site  Google Scholar
 J. Cao, Z. Lin, G. Huang, and N. Liu, “Voting based extreme learning machine,” Information Sciences, vol. 185, no. 1, pp. 66–77, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 S. C. Yan, D. Xu, B. Zhang, H.J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: a general framework for dimensionality reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40–51, 2007. View at: Publisher Site  Google Scholar
 D. Liang, J. Yang, Z. L. Zheng, and Y. C. Chang, “A facial expression recognition system based on supervised locally linear embedding,” Pattern Recognition Letters, vol. 26, no. 15, pp. 2374–2389, 2005. View at: Publisher Site  Google Scholar
 X. F. He and P. Niyogi, “Locality preserving projections,” in Proceedings of Advances Neural Information Processing Systems, pp. 153–160, 2003. View at: Google Scholar
 W. H. Greub, Multilinear Algebra, Springer, New York, NY, USA, 2nd edition, 1978. View at: MathSciNet
 M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, “Coding facial expressions with Gabor wavelets,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205, 1998. View at: Google Scholar
 T. Kanade, J. Cohn, and Y. Tian, “Comprehensive database for facial expression analysis,” in Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53, Grenoble, France, 2000. View at: Publisher Site  Google Scholar
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Copyright © 2014 Gaoyun An et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.