People Re-identification using Deep Convolutional Neural Network
Guanwen Zhang, Jien Kato, Yu Wang and Kenji Mase
Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Japan
Keywords:
People Re-identification, Deep Convolutional Neural Network, Linear SVM.
Abstract:
One key issue for people re-identification is to find good features or representation to bridge the gaps among
different appearances of the same people, which is introduced by large variances in view point, illumination
and non-rigid deformation. In this paper, we create a deep convolutional neural network (deep CNN) to solve
this problem and integrate feature learning and re-identification into one framework. In order to deal with
such ranking-like comparison problem, we introduce a linear support vector machine (linear SVM) to replace
conventional softmax activation function. Instead of learning cross-entropy loss, we adopt a margin-based
loss of pair-wise image to measure the similarity of the comparing pair. Although the proposed model is quite
simple, the experimental result shows encouraging performance of our method.
1 INTRODUCTION
People re-identification refers to the problems that,
recognize peoplewhen he/she leaves one camera view
and enters another camera view, or recognize peo-
ple when he/she reappears in the same field of view,
which is crucial for inter-camera tracking and for
understanding people behavior across camera net-
work. It is a valuable task in video surveillance
system and receives more and more attention with
the spreading camera networks installation (Zheng
et al., 2013)(Kviatkovsky et al., 2013)(Zhao et al.,
2013)(Farenzena et al., 2010).
Due to low image resolution and the long distance
between people and camera, biological information
such as people’s face or gait is general unavailable
for people re-identification. In addition, because of
the crossing camera issues, continuous visual track-
ing and intra-camera motion information of people
cannot be immediately utilized. Therefore, in the
current literature, studies on person re-identification
mainly focus on analyzing the people appearance,
with the acceptable assumption that people will not
change their clothing during the observation period.
The challenge in such an appearance based approach
principally comes from appearance variance induced
by light illumination, camera views, and non-rigid de-
formation of posture. This leads to the intra-camera
variance being even larger than the inter-camera vari-
ance, that is, the same people could look considerably
different in the videos captured by different cameras,
Figure 1: Selected images from VIPeR and CAVIAR4REID
dataset. The people’s appearance change with the variations
in posture, illumination, and resolution. Even the same peo-
ple look considerably different.
whereas different people could look extremely similar
in the videos captured by the same camera.
Exiting researches, which are trying to bridge the
“gap” between the different appearances of the same
people, can be roughly divided into two groups. The
first group focuses on extracting discriminative ap-
pearance of people to form a stable feature represen-
tation. In these studies, the global and local features,
such as color (Gray and Tao, 2008), shape (Wang
et al., 2007), or texture (Bazzani et al., 2012), are
integrated over images. Spatial information, such as
pictorial structure (Cheng et al., 2011), co-occurrence
representation (Wang et al., 2007), symmetry factors
(Farenzena et al., 2010) and salience (Zhao et al.,
216
Zhang G., Kato J., Wang Y. and Mase K..
People Re-identification using Deep Convolutional Neural Network.
DOI: 10.5220/0004740302160223
In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 216-223
ISBN: 978-989-758-009-3
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

2013), are also incorporated to deal with the lack of
spatial information of histogram description. The sec-
ond group, including a few researches, is working
on measuring the similarity between representations.
These methods, such as the well-known relative dis-
tance comparison (RDC) (Zheng et al., 2013), local
aligned feature transform (Li and Wang, 2013), local
distance comparison (Zhang et al., 2012) and large
margin nearest neighbor with rejection (Dikmen et al.,
2010), also achieve good performance.
All of the existing methods perform the higher
and complex model by using or being based on hand-
crafted features. A great amount of time and manual
work will be needed for different specific target re-
identification task. Therefore, an end-to-end solution
for re-identification task will be very valuable. As the
great improvement has been achieved by deep learn-
ing in various tasks (Krizhevsky et al., 2012)(Ser-
manet et al., 2012), representation learning from raw
input image seems to be a promising technique for
people re-identification work.
In this paper, we try to utilize deep convolu-
tional neural network (deep CNN) to solve people re-
identification problem, and thus incorporate feature
learning and re-identification into one framework. In
practical use, people re-identification needs perform-
ing ranking-likecomparison. In order to measure sim-
ilarity of the comparing pair, we introduce linear sup-
port vector machine (linear SVM) to replace the tradi-
tional softmax activation function. Instead of learning
cross-entropy loss for predicting class label, we mea-
sure the distance to the decision boundary that is more
suitable for re-identification task. Our work follows
the conventional approaches and pre-trains the lay-
ers with a unsupervised learning method in a greedy
layer-wise manner. Dropout technique is also adopted
in supervised learning phase to overcome the overfit-
ting problem. Although the model we used is quite
simple and results have not reached those of the state-
of-the-art methods, the experiments on public datasets
still show encourage performance.
There are three kinds of contribution in this paper:
(1) we proposed a simple architecture of deep CNN
for people re-identification problem that has not been
addressed before; (2) we introduced linear SVM on
the top of the network to measure the ranking com-
parison that is needed by people re-identification; (3)
we gave a detailed discussion about the limitation of
using deep learning in re-identification problem and
the potential for further improvement.
The rest of the paper is organized as follows. The
details of our approach are discussed in Section 2. We
first explain the architecture of deep CNN (2.1). The
linear SVM is then introduced in (2.2). At the end of
this section, we briefly describe unsupervised learn-
ing and dropout techniques (2.3). Experimental re-
sults are discussed in Section 3. Finally, we present
our conclusions and future perspectives in Section 4.
2 METHODOLOGY
People re-identification needs measuring the similar-
ity of the comparing images. Multiple comparison
results in a ranking list. Those with the highest sim-
ilarity are selected as the results. The deep CNNs
that have been reported so far are generally work-
ing on classification problems rather than compari-
son problems. In order to apply the deep CNN to
re-identification problem, we design the architecture
with two input branches, and introduce a linear sup-
port vector machine to measure the similarity of two
input images.
In following sections, we give details about the ar-
chitecture of our proposed network, and describe the
techniques we used in network training.
2.1 Architecture
The architecture of our deep CNN is summarized in
Fig.1. It contains 8 layers with three convolutional
layers (C1, C3 and C5), three subsampling layers (S2,
S4 and S6), one fully-connected layer (F7) and one
weighting layer (W8). One subsampling layer follows
one convolutional layer with local connection. S6 and
F7 are fully-connected. F7 and W8 play the function
of a linear SVM together. In the training phase, our
deep CNN minimizes the squared hinge loss of the
linear SVM that is equivalent to finding the max mar-
gin according to the true match (+1) and false match
(-1) over training sample pair. In the testing phase, the
similarity of the input image pair could be measured
by the distance to the decision boundary.
The two images, with the R, G, B channel, are lo-
cally connected to the first convolutional layer (C1)
through two different branches. One kernel in this
convolutional layer is working on the three channels
simultaneously and produces one channel. There are
32 kernels in this layer (in each branch) that pro-
duce 32 output channels totally. The neurons in
the second convolutional (C3) layer are connected to
both branches in the previous subsampling layer (S2),
which follows the first convolutional layer (C1). The
outputs of the kernels of two branches are simply
added together as the kernel maps. Another convo-
lutional and subsampling pair (C5 and S6), with more
kernels, follows the second subsampling layer (S4).
PeopleRe-identificationusingDeepConvolutionalNeuralNetwork
217

Figure 2: An illustration of the architecture of our deep CNN. There are two branches used for input of comparing pair. One
subsampling layer follows one convolutional layer, and works as a pair. The last two layers are fully connected. A linear
SVM classifier is added at top to replace the softmax layer for measuring similarity of input images pair.
The neurons in the fully-connected layer (F7) are con-
nected to all neurons in the third subsampling layer
(S6). The rectified-linearity is applied to the neurons
of every convolutional layer. We use the max-pooling
in both two branches in the first subsampling layer
(S2), and use the average-pooling in the second and
third subsampling layers (S4 and S6).
The first convolutional layer (C1) filters 32x32x3
input images with 32 kernels of size 5x5x3 with a
stride 1 pixels (for every convolution, we pad the pre-
vious kernel map with a 2-pixel border of zeros). The
input for the two branches in the first subsampling
(S2) layer is 32 kernel maps with size of 32x32. Sub-
sampling is performed for each channel and thus the
number of output channels is equal to number of in-
put. We use the filter with size of 3x3 by 2-pixel stride
to reduce the input kernel map into half size. The sec-
ond convolutional layer (C3) takes the both outputs of
subsampling (S2) on two branches as the input, and
filters it with 32 kernels of size 16x16x2 by 1 pix-
els stride. The third convolutional layer (C5) has 128
kernels of size 8x8x32, and the fully-connected layer
(F7) has the 2048 nodes with fully connection to the
4x4x128 neurons in the third subsampling layer (S6).
The processing in the second and third subsampling
layers (S4 and S6) is performed as same as the first
subsampling layer (S2) and is down-sampled by a fac-
tor of 2.
2.2 Softmax vs. Linear SVM
In conventional methods, it is popular to use the
softmax activate function in the top layer to predict
the classes. Assume there is a fully connection be-
tween softmax and penultimate layer and we have a N
classes to predict, the number of the nodes in softmax
layer will have the same number N as the classes. Let
h
j
be the activation of node j in penultimate layer, and
W
ji
be the weighting of the connection between node
j in penultimate layer and node i in softmax layer.
Since there is a fully connection, the input for the
node i in softmax layer is then given by a
i
=
∑
j
h
j
W
ji
.
The probability of class i, i.e. the output of node i, is
defined as:
p
i
=
exp(a
i
)
∑
N
k
exp(a
k
)
, (1)
and
∑
N
i
p
i
= 1. In this case, the predicted class label
would be
b
i = argmax(p
i
).
In this paper, in order to measure similarity of
the comparing pair, we introduce linear support vec-
tor machine to replace the softmax layer. Given the
training data {x
n
,t
n
}
N
n=1
, where x
n
∈ R
D
and t
n
∈
{−1, +1}, the linear support vector machine (linear
SVM) could be formulated as the following optimiza-
tion problem:
obj(w) =
1
2
w
T
w+C
N
∑
n=1
(max(1− w
T
x
n
t
n
,0))
2
. (2)
This is known as L2-SVM, which is a popular opti-
mization of SVM due to its differentiable and harder
punishment of violating samples. The predicted class
label could be obtain by
b
t = argmax(w
T
x)t.
In order to use the objective function as the super-
vised learning to train the parameters in low layers,
we should back propagate the gradients of the linear
SVM. The differentiate of the linear SVM, with re-
spective to w, is given by
∂obj(w)
∂w
= w− 2Cx
n
t
n
(max(1− w
T
x
n
t
n
,0)). (3)
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By introducing activation, h = (h
1
,. .., h
2048
)
T
, of the
neurons in penultimate layer (F7), the differentiate for
each activation h
i
is given by
∂obj(w)
∂h
i
= w− 2Ch
i
t
n
(max(1− w
T
h
i
t
n
,0)), (4)
where t
n
indicates true or false match of the input
pair. By using such gradient to replace the gradient
of softmax function, we could use the same back-
propagation algorithm as in traditional deep learning
method to train the parameters for each layer.
We have noted that the same strategy is also used
in (Zhong et al., 2000)(Nagiet al., 2012)(Tang, 2013).
However, rather than on the class label of the input
data, we focus more on the distance of input pair to
the decision boundary, where larger distance indicates
the higher similarity.
2.3 Unsupervised Learning and
Dropout
Generally, there are only a few training data in the
re-identification task. However, our neural network
architecture has thousands of parameters. Therefore,
it is difficult to learn so many parameters without con-
siderable overfitting. Below, we describe two primary
ways in which we combat overfitting problem.
2.3.1 Unsupervised Learning
In conventional approaches for training a deep net-
work, the unknown parameters are first randomly ini-
tialized, and then learned by directly searching gra-
dient descent of supervised objective function. How-
ever this kind of methods often leads to local min-
imum and performance gets worse as the depth of
network increasing. Hinton et al. proposed a greedy
layer-wise unsupervised pre-training to deal with such
problem (Hinton and Salakhutdinov, 2006). They
used unsupervised learning algorithm to pre-train
each layer, where out of the previous layer is used
as input for training following layer. After the pre-
training stage, the whole network is fine-tuned and
finally realizes the optimization with a global su-
pervised objective function. In this way, the unsu-
pervised learning, working as initializing parameters
phase, leads to much better solution in term of gener-
alization performance.
In this paper, following the similar idea of (Le-
Cun et al., 2010)(Sermanet et al., 2012), we use Pre-
dictive Sparse Decomposition (PSD) as unsupervised
learning method to pre-train each layer of our deep
CNN. PSD approximates the inputs as a sparse linear
transformation on a dictionary. Similar as sparse cod-
ing, the sparse representation Z
∗
could be obtained by
minimizing the energy function as follows:
E(Z,W,K) = kX −WZk
2
2
+ λkZk
1
+ kZ −C(X,K)k
2
2
(5)
where X is the input image, K is the filters in current
layer that we want to learn and matrixW is the dictio-
nary which is randomly initialized. The unsupervised
learning is processing in two steps: in the first step,
we find sparse representation Z
∗
and in the second
step, we update the dictionary matrix W and filters K.
The details can be referred to (Sermanet et al., 2012)
and (LeCun et al., 2010).
2.3.2 Dropout
Dropout is an efficient technique introduced in (Hin-
ton et al., 2012) which can reduce the generalization
error of deep architecture neural network. Similar
as denoising AutoEncoder, dropout randomly select
a fraction of neurons in the hidden layers, and force
them to be inactivated by setting the noise zeros. The
selected neurons in every epoch do not contribute to
the forward pass and also do not participate in back-
propagation. However, unlike in denoising AutoEn-
coder, the dropout is performed in supervised training
and could be used in all layers in a deep neural net-
work for different purposes (Hinton et al., 2012). In
this way, the neural network samples the network with
different connectivity patterns, but all these architec-
tures could share weights by the neurons that are not
dropped out. By this kind of randomly sampling, the
network is likely forced to learn an averaging model
and finally achieve a robust model to battle against
overfitting. We use dropout in the third convolutional
layer (C5) and fully-connected layer (F7). We choose
0.5 as the dropout rate of parameters.
3 EXPERIMENT
3.1 Dataset Description
We evaluated our proposed deep CNN by applying
it to two public datasets VIPeR of Douglas et al.
(Gray et al., 2007) and CAVIAR4REID of Cheng et
al. (Cheng et al., 2011). These datasets cover different
genres and include different people postures, under
a variety of illumination conditions, with various de-
grees of occlusion and camera resolution. Therefore,
they are very challenging. VIPeR is originally created
for viewpoint invariant pedestrian recognition. There
are 632 image pairs in this dataset which are captured
by two camera views in outdoor environment. Due
to the arbitrary change of the view point under vary-
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219

0 20 40 60 80 100
0
10
20
30
40
50
60
70
80
90
100
Rank score
Recognition percentage
VIPeR CMC Curve
PRC
PLS
Bhat.
Li’s
ITML
OURS
5 10 15 20 25 30
0
10
20
30
40
50
60
70
80
90
100
Rank score
Recognition percentage
CAVIAR4REID CMC Curve
L2
Li’s
ITML
OURS
Figure 3: Evaluation of CMC curve for VIPeR and CAVIAR4REID dataset with the gallery size as 316 and 36 respectively.
The state-of-the-art and base line methods are used as comparison. The corresponding results are obtained from the public
papers.
Table 1: Matching rate of top ranking(%) on VIPeR dataset, gallery size is 316. The bold and red typeface are used to
highlight the best results and the baseline results. Our results are shown in first the row.
Method Top 1 Top 10 Top 20 Top 30 Top 40 Top 50 Top 60 Top 80 Top 100
OURS 12.5 26.3 39.7 52.6 62.1 67.9 74.5 85.0 90.4
RDC 15.7 53.9 70.1 79.4 83.5 87.4 90.2 92.7 96.7
PLS
2.7 10.9 17.3 24.3 28.6 32.2 38.1 48.4 53.8
Xing’s 4.6 16.6 24.4 30.4 33.9 39.2 44.5 54.8 61.2
L1-norm
4.2 16.5 23.8 29.7 32.4 37.7 42.6 50.1 56.7
Li’s 29.6 69.3 82.3 91.7 94.6 96.8 97.6 99.1 100
ITML
12.4 39.7 55.2 66.3 72.9 78.7 82.5 87.2 91.3
ing illumination between two camera views, appear-
ance of the samples in an image pair has great differ-
ence. CAVIAR4REID is built specifically for person
re-identification tasks. It consists of 72 people with
1,220 images. 50 people are captured in two camera
views, and the remaining 22 are captured in a single
camera view. The images in this dataset are selected
by maximizing the variance with respect to the res-
olution, illumination, occlusion and posture (Cheng
et al., 2011).
3.2 Evaluation Method
We randomly divide each dataset into a training set
and a testing set according to people’s number. The
training samples in each training set consist of two
kinds of pairs: true pair and false pair. Given an im-
age from one camera view, the true pair is created by
selecting the image of the same people in the other
view, while the false is created by randomly select-
ing the image of a different people from the other
view. Since there are multiple images for one per-
son in CAVIAR4REID dataset, the training pairs are
created by selecting all images of each person. In this
way, we have 632 and around 500 training pairs for
VIPeR and CAVIAR4REID datasets respectively.
Each testing set is composed of a gallery set and
a probe set. The gallery set consists of one image of
each person, and the remaining images are used as
the probe set. Re-identification is conducted by find-
ing a match from the gallery for each probe. During
experiments, this procedure is repeated 5 times to ob-
tain average performance. The popular evaluation cri-
terion, Cumulative Matching Characteristic (CMC),
which represents the expectation of finding the cor-
rect match in the top n candidates, is used to measure
matching rates.
Our proposed method is implemented by C++ and
CUDA with library of Python. The convolution work
is based on the CUDA kernels published by Alex
Krizhevsky
1
. The graphic card we use in the experi-
ments is NVIDIA GTX 660.
In the training phase, the initial parameters are set
by a uniform distribution in the range [−0.05, 0.05].
We also initialize the basis in the convolutional layers
with the constant 0.002 and set the momentum as 0.9,
weight decay as 0.004 and batch size as 50. We use all
training pairs of two datasets for the pre-trained unsu-
1
http://code.google.com/p/cuda-convnet
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Table 2: Matching rate of top ranking(%) on CAVIAR4REID dataset, gallery size is 36. The bold and red typeface are used
to highlight the best results and the baseline results. Our results are shown in first the row.
Method Top 1 Top 5 Top 10 Top 15 Top 20 Top 25 Top 30
OURS 7.2 26.9 42.6 57.5 76.25 86.5 92.6
L2
4.1 21.5 37.8 44.6 53.2 61.4 68.6
PS 8.5 32 48.8 59.7 66.4 79.7 86.6
SDALF
6.8 25 45 55 64.5 74 83
Li’s 10.2 39 59 71.4 79.5 84.4 88.2
ITML
7.3 32.49 50.5 61.3 70.4 77.8 82
0 50 100 150 200 250 300 350 400 450 500
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Epochs
Recognition Error Rate
VIPeR
Testing Error (N)
Training Error (N)
Testing Error (Y)
Training Error (Y)
0 50 100 150 200 250 300 350 400 450 500
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Epochs
Recognition Error Rate
CAVIAR4REID
Testing Error (N)
Training Error (N)
Testing Error (Y)
Training Error (Y)
Figure 4: Training error rate and testing error rate of the identification on VIPeR and CAVIAR4REID dataset. They are
evaluated by USING pre-trained unsupervised learning and dropout (Y), and NOT USING these techniques (N). The testing
errors are obtained by every 10 epochs.
pervised learning. The dropout technique is only used
in fine-tune stage, and the dropout rate is set as 0.5.
During fine-tune stage for specific target training set,
we follow the similar strategy as (Krizhevsky et al.,
2012) and manually adjust learning rate throughout
training.
3.3 Results
In experiments, we only choose the learning-based
method that works on the two datasets. We com-
pare our proposed method with the baseline meth-
ods, partial least squares (PLS) (Schwartz and Davis,
2009) and Bhattacharyya distance learning (Bhat.), on
VIPeR, and compare with baseline method, Euclidean
distance learning (L2), on CAVIAR4REID. We also
compare our method with some well-known state-of-
the-art methods such as relative distance comparison
(RDC) (Zheng et al., 2013), information theoretical
metric learning (ITML) and the method proposed by
Li et al. (Li’s) (Li and Wang, 2013). The results
of above mentioned methods are obtained from the
published papers (Zheng et al., 2013)(Li and Wang,
2013).
The comparison results are shown in Fig.3 and Ta-
ble 1 and 2. It can be seen that our proposed model
outperforms the baseline methods on these datasets,
but it is still worse than the state-of-the-art methods.
Notice that the CMC curve of our method gets com-
petitive after rank 15 on the CAVIAR4REID. The size
of training pairs of VIPeR and CAVIAR4REID are
632 and around 500 (people with 10 images may be
selected as training set), respectively. However, the
number of people, that will be re-identified, is 316 and
36. Therefore, the relative number of training pairs
for each person in VIPeR is 2 (= 632/316), much
smaller than 14 (= 500/36) in CAVIAR4REID. This
makes the performance of CAVIAR4REID is better
than that of VIPeR.
We also notice that, although we have introduced
pre-trained unsupervised learning and dropout tech-
nique, overfitting is still severe. In order to show the
details, we give a comparison of training and testing
errors obtained by using or not using unsupervised
learning and dropout technique. The testing errors
are obtained from a validation set, which is created
in the same way as training set, but using the im-
ages of the people in testing set. In Fig.4, it can be
seen clearly that, without using unsupervised learn-
ing and dropout technique, the training errors on both
datasets are close to zeros, while the testing errors
are quite high. By introducing unsupervised learning
and dropout technique, the divergence between train-
ing and testing error reduces on both datasets. Never-
theless, the higher and wild swing testing errors still
reveal the networks suffer from overfitting.
Increasing the training data is essential way to re-
duce overfitting and improve performance of the net-
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work. However, for re-identification task, since a pos-
itive sample (true match) consists of images from the
same people, the number of the people in the dataset
restricts the number of the positive samples. This
makes the number of positive samples usually much
smaller than that of negative samples. In the training
phase, the negative samples should be limited within
a certain amount to avoid overfitting.
Multiple-shot dataset of re-identification task is a
good choice for solving this problem. As shown in ex-
perimental results, by creating more positive samples
crossing multiple images, the performance of network
on CAVIAR4REID is better than that on VIPeR. Dur-
ing the experiments, we observe that the performance
of the network on some multiple shot datasets, such
as ETHZ (Ess et al., 2007) and Person Re-ID 2011
(Hirzer et al., 2011), is not impressive. By further in-
specting these datasets, we find that multiple images
of the people are extracted from video sequences. The
difference between images is small, and they cannot
contribute to training work.
From another point of view, similar as denoising
AutoEncoder, it is possible to increase positive sam-
ples by applying some transformations to original im-
ages, such as partial corruption of the input image
pairs, extracting random patch of the images and so
on. By such a strategy, we can not only increase pos-
itive samples to combat overfitting issue, but also can
improve the robustness of the network against noise.
4 CONCLUSIONS
How to find a good feature representation to bridge
the “gap” between appearances of the same people
is a very challenging task. Existing methods either
employ hand craft features or use machine learning
method with existing features to form a specific rep-
resentation. However, there are a lot of uncertainty
in these methods due to human factors and specific
applications. Deep learning, with ability to learn a
proper feature representation from the bottom of the
raw images, seems to be a promising solution for the
people re-identification tasks.
In this paper, we utilize deep convolutional neu-
ral network to solve people re-identification problem.
We integrate feature learning and re-identification
into one framework, and accomplish learning and re-
identification simultaneously. In order to deal with
the ranking-like comparison problem, we introduce
a linear support vector machine to replace the soft-
max lay for measuring the similarity of the comparing
images. Since there is a large amount of parameters
of the network needed to be estimated, while only a
small number of training data are available, the pre-
trained unsupervised learning and dropout technique
are used to reduce overfitting.
Although the proposed is quite simple, we still
achieve very encourage performance compared with
baseline methods, which gives us great confidence.
But compared with the state-of-the-art methods, our
performance needs to be further improved. The care-
ful analysis on the results shows that the serious over-
fitting caused by the lack of positive training samples
seems to be the reason. This is our future work.
ACKNOWLEDGEMENTS
This research was supported by the National In-
stitute of Information and Communication Technol-
ogy (NICT), and by the Strategic Information and
Communications R&D Promotion Programme (No.
131306004). Yu Wang is supported by Grant-in-Aid
for Japan Society for the Promotion of Science and
Guanwen Zhang is also supported by the Fund of the
China Scholarship Council.
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