CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation — TEI-tagged view

Wen-Jing YanpersName, Xiaobai LipersName, Su-Jing WangpersName, Guoying ZhaopersName, Yong-Jin LiupersName, Yu-Hsin ChenpersName, Xiaolan FupersName (2014date). “CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation.” PLoS ONEtitle·j.

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Contribution — CASME IIterm high-resolution spontaneous micro-expressionterm database and baseline

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Abstract. Abstract A robust automatic micro-expressionterm recognition system would have broad applications in national safety, police interrogation, and clinical diagnosis. Developing such a system requires high quality databases with sufficient training samples which are currently not available. We reviewed the previously developed micro-expressionterm databases and built an improved one (CASME IIterm), with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area). We elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination (such as removing light flickering). Among nearly 3000 facial movements, 247 micro-expressionsterm were selected for the database with action unitsterm (AUs) and emotions labeled. For baseline evaluation, LBP-TOPterm and SVM were employed respectively for feature extraction and classifier with the leave-one-subject-out cross-validationterm method. The best performance is 63.41% for 5-class classification. Introduction A micro-expressionterm is a brief facial movement which reveals a genuine emotion that a person tries to conceal [1] – [3] . In addition, micro-expressionsterm might be unaware and/or uncontrollable to the actor, thus may provide effective clues for detecting lies. Therefore, micro-expressionterm recognition has many potential applications such as clinical diagnosis and interrogation. In the clinical field, micro-expressionsterm may be used for understanding genuine emotions of the patients and promoting better therapies. For instance, Ekman [4] analyzed an interview video of a patient stricken with depression and that patient displayed a desperate micro-expressionterm which could predict a commit suicide. During an interrogation or interview, micro-expressionterm discloses what the interviewee really feels and thus helps further investigation. For instance, a micro-expressionterm of scorn from Kato Kaolin's testimony in the O.J. Simpson trial betrayed his genuine feelings [5] . A micro-expressionterm is featured by its short duration. The generally accepted upper limit of the duration is 1/2 s [3] , [6] . These fleeting facial expressions, as observed, are usually low in intensity – it might be so brief for the facial muscles to become fully-stretched with suppression. Because of the short duration and low intensity, it is usually imperceptible or neglected by the naked eyes [1] . To better analyze micro-expressionsterm and to help reveal people's feelings, an automatic micro-expressionterm recognition system is in great need. Automatic facial expression recognition is booming. Researchers had developed many algorithms and the accuracy has reached over 90% for the posed six basic facial expressions (anger, disgust, fear, happiness, sadness and surprise). On the contrary, studies on automatic micro-expressionterm recognition have just started recently and only several pieces of work were available. Shreve et al. [7] divided the face into sub-regions (mouth, cheeks, forehead, and eyes) and calculated the facial strain of each sub-region. The strain pattern in each sub-region is then analyzed to detect micro-expressionterm from the video clips. Pfister et al. [8] proposed a framework using temporal interpolation model and multiple kernel learning to recognize micro-expressionsterm. Polikovsky et al. [9] used a 3D-gradient descriptor for micro-expressionterm recognition. Wu et al. [10] developed an automatic micro-expressionterm recognition system by employing Gabor features and using GentleSVM as the classifier. WangpersName et al. [11] treated a gray-scale micro-expressionterm video clip as a 3rd-order tensor and utilized Discriminant Tensor Subspace Analysis (DTSA) and Extreme Learning Machine (ELM) to recognize micro-expressionsterm. Ruiz-Hernandez and Pietikäinen [12] proposed to encode the Local Binary Patternsterm·def (LBP) using a re-parametrization of the second local order Gaussian jet to generate more robust and reliable histograms for micro-expressionterm representation. The success in conventional facial expression recognition largely rely on sufficient facial expression databases, such as the popularly used CK+ [13] , MUG [14] , MMI [15] , JAFFE [16] , Multi-PIE [17] , and also several 3-D facial expression databases [18] , [19] . In contrast, there are very few well-developed micro-expressionterm databases, which hindered the development of micro-expressionterm recognition research. Eliciting micro-expressionterm is difficult because it only presents at certain situations when an individual tries to suppress felt emotions but fails. According to Ekman, when a person tries to conceal his or her feelings, the true emotions would leak quickly and may be manifested as micro-expressionsterm (Ekman & Friesen, 1969). In the early years, researchers constructed high-stakes situations to elicit micro-expressionsterm, such as by asking people to lie about what they saw in video episodes [20] , and by constructing crime scenarios (mock theft) and opinion scenarios (lie about own opinions) [21] . Micro-expressionsterm elicited in these studies were commonly confounded by other facial movements (such as conversational facial movements), which are irrelevant to emotions and may “contaminate” the performance of the currently-not-robust micro-expressionterm analysis algorithms. Some researchers developed small databases of micro-expressionsterm by asking participants to pose facial expressions quickly [7] , [9] . However, these posed “micro-expressionsterm” are different from the spontaneous ones [2] , [3] . Therefore, spontaneous micro-expressionterm databases are necessary for academic research and practical applications. It was found that watching emotional video episodes while neutralizing faces is an effective method to elicit spontaneous micro-expressionsterm without many irrelevant facial movements [3] , [22] . Until now, there are only four micro-expressionterm databases and only two of them contain spontaneous micro-expressionsterm, as briefly summarized in Table 1 . Table 1 The current micro-expressionterm databases. USF-HD [7] Polikovsky's database [9] SMIC database [22] CASME database [23] subjects / 10 16 valid * 19 valid samples 100 42 164 195 Frames per sec. 30 200 100 60 Posed/spontaneous Posed Posed spontaneous spontaneous AU labels No No no yes Emotion class 6 6 3 7 remarks Their criteria set for micro- expressions (2/3 s) is longer than most accepted durations / Emotions are classified as positive, negative and surprise Tense and repression in addition to the basic emotions * Since not all of the participants showed micro-expressionsterm, the recruited were more than the valid. The first reason for developing a new database is that current spontaneous micro-expressionterm databases are too small to provide sufficient samples. A large number of samples is needed for robust automatic micro-expressionterm recognition research and related applications. We aim to provide more samples for training and test. The second reason is that the video quality of current micro-expressionterm databases cannot satisfy the need for micro-expressionterm analysis. Micro-expressionterm is brief in duration and low in intensity, which makes it difficult to detect and recognize. Higher spatial and temporal resolution may help to train the algorithms (which remains unclear). Compared with the 100 fps in SMIC and 60 fps in CASME, the sampling rate of CASME IIterm is 200 fps, aiming to provide more detailed information on the facial muscle movements. In addition, compared with the face size of about 190 pixels×230 pixels in SMIC and about 150 pixels×190 pixels in CASME, the samples in CASME IIterm have a larger face size about 280 pixels×340 pixels. Third, we tuned the instruction in elicitation to increase the variety of the micro-expressionsterm. Ekman (1969) mentioned two types of micro displays which may leak individual's genuine feelings: “The time-reduced full affect micro displays (i.e. micro-expressionsterm) may well be those which the ego is not aware of, while the squelched micro displays may be those which the ego senses and interrupts in mid performance.” The elicitation paradigm used by Li et al. [22] and YanpersName et al. [23] only focused on the former, i.e. asking participants to fully suppress facial movements. The other type of micro-expressionterm elicitation paradigm, i.e. asking participants to suppress facial movements when they are self-aware, is not used for collecting micro-expressionsterm. It is possible that these two types of micro-expressionsterm have different dynamic features. We aim to give participants different instructions to elicit both types of micro-expressionsterm in order to increase the variety of our data. This paper consists of two parts. In the first part, we introduce the procedure of developing CASME IIterm, including methods of eliciting micro-expressionsterm and how we select samples for the database. In the second part, we evaluate this database with Local Binary Patternsterm·def from Three Orthogonal Planes (LBP-TOPterm) for feature extraction and Support Vector Machineterm (SVM) for classification. Methods: Elicitation and analysis The method of micro-expressionterm elicitation Ethics statement The experimental procedure was approved by the IRB of the Institute of Psychology, Chinese Academy of Sciences. The participants signed informed consent and had the right to quit the experiment at any time. The subject of the photograph has given written informed consent, as outlined in the PLOS consent form, to publication of their photograph. Participants Thirty-five participants were recruited, with a mean age of 22.03 years (Standard Deviation (SD) = 1.60) in the study. They all signed informed consent and had the right to quit the experiment at any time. Apparatus Four strictly selected LED lamps (to avoid possible flickering light caused by alternating current, 50 Hz) under umbrella reflectors (to focus the light on the participant's face) are placed to provide steady and high-intensity illumination. We designed such a setup because flickering light usually appear in high speed recordings (such as those in SMIC) and the video recorded usually turn out to be dark and noisy. We used Point Grey GRAS-03K2C camera to capture participants' faces, with the resolution at 640×480 pixels. “Raw 8” mode is used to reach 200 fps. We saved recordings as MJPEG format without any inter-frame compression. Materials Videos episodes with high emotional valence proved to be effective materials for eliciting micro-expressionsterm [3] , [22] , [23] . Video episodes have a relatively high degree of ecological validity (Gross & Robert, 1995) and are usually better than pictures in term of emotional valence. Furthermore, video episodes are lasting and dynamic emotional stimuli, making inhibition more difficult (YanpersName et al., 2013). We used video clips similar to those for eliciting micro-expressionsterm in CASME, but several invalid episodes were removed and several new ones were added. 20 participants rated the video episodes by choosing one or two emotion key words from a list and rating the intensity on a 7-point Likert scale (0 as the lowest and 6 as the highest). If a certain basic expression (e.g. happiness) was chosen by one third of the participants or more for a video episode, that emotion would be assumed as the principal emotion(s) of the video episode. There are individual differences of the subjective feelings at the same video episodes. Meanwhile, one episode may elicit several emotions, which is clearly exemplified by episode #12 and #17. Details of selected video episodes and their corresponding rating scores are listed in Table 2 . Table 2 Video episodes used for eliciting micro-expressionsterm and participants' rating scores. Episode NO. Content Duration Main elicited emotion(s) Rate of selection Mean score (intensity) 1 Ad of 7 Up 2′52″ happiness 0.69 3.27 2 Sports fault 1′18″ happiness 0.71 3.60 3 Jokes on names 51′ happiness 0.70 3.14 4 Larva (animation) 1′32″ happiness 0.64 4.43 5 Larva 2 (animation) 1′32″ happiness 0.94 3.93 6 Jokes on stool 1′28″ disgust 0.81 4.15 7 Tooth extraction 1′7″ disgust 0.69 4.18 8 Eating worm 1′35″ disgust 0.78 4.00 9 A brazen man 1′34″ disgust 0.81 3.23 10 Operation for Near-sighted eye 1′56″ fear 0.63 2.90 11 Ring(Horror film) 2′4″ fear 0.67 2.83 12 Meat grinder 2′25″ disgust (fear) 0.60(0.33) 3.78(3.6) 13 Final destination(movie) 1′43″ fear 0.5 3.72 14 Roots and Branches (Movie) 2′26″ sadness 1.00 4.27 15 Derek Redmond in 1992 Olympics game 1′31″ sadness 0.71 4.08 16 A girl killed by cars 1′57″ sadness 1.00 5.00 17 Train accident and officer's dereliction 1′57″ anger (sadness) 0.69 (0.61) 4.33(4.88) 18 Torturing dog 1′37″ anger 0.75 4.67 19 Beating a pregnant women 2′25″ anger 0.94 4.93 Procedure To elicit such facial expressions, we induced participants to experience a high arousal and promoted a motivation to disguise. Similar procedures were conducted as for building SMIC and CASME (see Figure 1 ). The difference is on the instructions given to the participants: 18 participants were asked to keep neutralized faces when watching video clips while 17 participants only tried to suppress the facial movements when they realized there was a facial expression. This design was for eliciting two types of micro-expressionterm [1] as explained above in the Introduction . The participants were asked to watch the video clips in front of a screen and avoid any body movement. The experimenter monitored participants' face on-line from the other monitor. This setup helped the experimenter to pre-define some habitual movements which will be verified by the participant after each video play finished. Figure 1 Acquisition setup for elicitation and recording of micro-expressionsterm. Sample selection and category labeling Two coders were involved in the analysis of the micro-expressionsterm. We processed the raw video recordings in following steps to select micro-expressionsterm: Step 1 Remove the irrelevant facial movements. There is a large quantity of facial movements in the recordings but many are obviously irrelevant, such as head movements, pressing the lips when swallowing saliva or other habitual movements (such as blowing the nose). All the unemotional movements were ruled out. Step 2 Pre-select micro-expressionterm candidates by restricting the to-be-analyzed samples. The coders browsed and spotted the onset and offset frames of each emotional movement clip and only those whose duration last less than one second were saved as micro-expressionterm candidates for a further coding. Expressions that are too subtle to be precisely coded are also excluded. Step 3 Candidate micro-expressionterm clips were converted into frame sequences to facilitate precise labeling of onset and offset frames. By employing a frame-by-frame approach, the precise frame numbers of onset and offset of each sequence were decided, and only those met the selection criteria (total duration less than 500 ms or onset duration less than 250 ms) were chosen as the final micro-expressionterm samples. After the micro-expressionsterm were selected, AU(s) were marked and emotion labels were given for each micro-expressionterm by two coders. The reliability is 0.846, which is calculated by: where # AU(C 1 C 2 ) is the number of AUs on which Coder 1 and Coder 2 agreed and # All_AU is the total number of AUs in a micro-expressionterm scored by the two coders. Afterwards, the two coders discussed and arbitrated the disagreements. FACSterm·def [10] is an objective method for labeling facial movements in terms of component actions. Different groups may have different micro-expressionterm classes but could have the same AU coding system. Unlike posed facial expressions, for which people were asked to generate several preset facial movements, the spontaneous micro-expressionsterm in this database were elicited by strong emotional stimuli. Therefore, it is inappropriate to forcefully classify these micro-expressionsterm into the same six categories as for ordinary facial expressions. For example, AU4 (frown) may indicate disgust, anger, attention or tense [3] . In CASME IIterm database, the micro-expressionsterm are labeled based on the AUs, participants' self-report, and the contents of the video episodes. We provided five main categories (as listed in Table 3 ). Table 3 Criteria for labeling the emotions and the frequency in the database * . Emotion Criteria N Happiness either AU6 or AU12 33 Disgust one of AU9, AU10 or AU4+AU7 60 Surprise AU1+2, AU25 or AU2 25 Repression AU15 or AU17 alone or in combination 27 Others Other emotion-related facial movements 102 * The emotion labeling are just partly based on the AUs because micro-expressionsterm are usually partial and in low intensity. Therefore, we also take account of participants' self-report and the content of the video episodes. Profile of the database The developed database called CASME IIterm contains 247 micro-expressionterm samples from 26 participants. They are selected from nearly 3,000 elicited facial movements. These samples are coded with the onset and offset frames, with action unitsterm (AUs) marked and emotions labeled and five main categories are provided, as listed in Table 3 . The criteria employed to label emotion categories are also shown in Table 3 , and one example of micro-expressionterm clip is illustrated in Figure 2 . Very subtle micro-expressionsterm were removed because they are almost unable to code the onset and the offset (difficult to detect the turning points due to the almost imperceptible changes). The CASME IIterm database has the following characteristics: Figure 2 A demonstration of the frame sequence in a micro-expressionterm. The apex frame presents at about 110-expression is 4+9 (with AU 17 kept almost unchanged), which indicates disgust. The three rectangles above the images show the right inner brow (AU 4) in “zoom in” mode. The movement is more obvious in video play than picture sequence. The samples are spontaneous and dynamic micro-expressionsterm. Baseline (usually neutral) frames are kept before and after each micro-expressionterm, making it possible to evaluate different detection algorithms. The recordings have high temporal resolution (200 fps) and relatively higher face resolution (280×340 pixels). Micro-expressionterm labeling is based on FACSterm·def investigator's guide [24] and YanpersName et al.'s findings [3] . The labeling criteria are different from the traditional 6 categories on ordinary facial expressions. The recordings have proper illumination without flickering light and with reduced highlight regions on the face. Some types of facial expressions are difficult to elicit in laboratory situations, thus the samples in different categories distributed unequally, e.g., there are 60 disgust samples but only 7 sadness samples. In CASME IIterm, we provide 5 classes of micro-expressionsterm. Database evaluation Preprocessing Before running any formal experiment on this dataset, three steps of preprocessing were carried out on the raw sample clips that have been labeled. Let be the set of micro-expressionterm clips. The i th sample , is the frame number of sequence . First, a frontal face image M with neutral expression was selected as the model face. 68 facial landmark points of the model face were detected using the Active Shape Model [25] . Second, the first frame of each micro-expressionterm clip was normalized to the model face using a Local Weighted Mean (LWM) [26] transformation, and the transform matrix T is written as: (1) Where is the coordinates of 68 landmark points of the first frame of the micro-expressionterm sample . Then all frames of were normalized using the same matrix . There are two reasons why we detected ASM landmark points only on the first frame but not on all frames. The first reason is that, since the time duration of a micro-expressionterm is very short the rigid head movement within the duration can be neglected. The second reason is that the landmark points detected by ASM may be not accurate enough; if applied on a sequence of frames, there could be big deviation of locations of the same point even when the face did not move at all. The normalized image was computed as a 2D transformation of the original image: (2) is the j th frame of the normalized micro-expressionterm sequence . Third, the eye coordinates of the first frame of each normalized micro-expressionterm sequence were located and then the face of each frame of was cropped out using a rectangle determined by eye positions . Experiments We carried out a micro-expressionterm recognition experiment on the samples preprocessed by the steps described in the previous section. LBP-TOPterm features were extracted to describe micro-expressionsterm from a spatiotemporal point of view, and SVM was employed as the classifier to provide baseline performance for the future evaluation of our database ( Figure 3 ). The details are described below. Figure 3 Block diagram of the micro-expressionterm classification. Methods Local Binary Patternsterm·def from Three Orthogonal Planes (LBP-TOPterm) The conventional LBP method has been demonstrated to be effective for describing 2D textures of static images [27] . Considering one pixel C in the image, LBP operator describes its local texture pattern by comparing and thresholding the gray values of its neighboring pixels. As illustrated in Figure 4(a) , for the center pixel C with its P neighboring pixels with the radius R, the LBP value is calculated as: (3) Where are the coordinates of the center pixel C and is its gray value, and is the gray value of its pth neighboring pixel on the radius R. is the weight corresponding to the neighboring pixels locations, which is used to transform a binary pattern string to a decimal index for this pattern. The function is a sign function defined as: (4) Figure 4 The LBP operator and its histogram. Given an input image of size M by N, the LBP value b of each pixel could be calculated using the above two equations. Then we can form a feature vector to represent the input image by calculating the histogram distribution of all of its LBPs: (5) Where n denotes the total number of interested patterns, and: (6) One example of LBP histogram is illustrated in Figure 4(b) . More details about LBP please refer to [27] . As an extension of the basic LBP idea, Local Binary Patternterm·def on Three Orthogonal Planes (LBP-TOPterm) was proposed by ZhaopersName et al [28] for dynamic texture analysis in the spatial-temporal domain. Given a video sequence of time length T, usually it can be thought as a stack of XY planes along the time axis T, but it can also be thought as a stack of XT planes on axis Y, or a stack of YT planes on axis X. The XT and YT planes provide information about the space-time transitions. Taking micro-expressionterm as an example, the XT and YT planes contain information of how the gray values of a row or a column of pixels change along the time dimension; if the input data volume is of a mouth corner, they describe the mouth movement as dynamic texture in the XT or YT plane accordingly (see Figure 5(a) ). Figure 5 The texture of three planes and the corresponding histograms. (a) XY, XT and YT planes of a micro-expressionterm sample, (b) concatenated LBP-TOPterm feature. The basic idea of LBP-TOPterm is the similar as LBP, which is to use an index code to represent the local pattern of each pixel by comparing (and thresholding) its gray value to its neighbors' gray values. To include information from 3D spatial-temporal domain, LBP code is extracted from the XY, XT and YT planes of each pixel, respectively. Then one histogram of each plane is formed by using equation (3) and (4) and then concatenated into a single histogram as the final LBP-TOPterm feature vector. Note that we can set different values for the radius of X, Y and T axis as , and , so finally we can have elliptical sampling than circular sampling. The procedure of extracting LBP-TOPterm feature histogram is demonstrated in Figure 5(b) . Results The radii in axes X and Y vary from 1 to 4. The radius of T varies from 2 to 4 (we don't consider T = 1 because the samples are in 200 fps where the change of two neighboring frames is little). The number P of the neighboring points in the XY, XT and YT planes was set as 4. SVM was used as the classifier. For this baseline evaluation, we selected 5 classes of the micro-expressionsterm–happiness, surprise, disgust, repression and others–for training and test. Considering samples unequally distributed in the five selected classes, leave-one-subject-out cross validation was applied. The LBP-TOPterm features were extracted in 5×5 blocks. The performance is shown in Table 4 . The best performance is 63.41% where the radii are 1, 1 and 4 for XY, YT and XT planes respectively. Table 4 Performance on recognizing 5-class micro-expressionsterm with LBP-TOPterm features extraction and leave-one-out cross-validation. R X R Y R T 5×5 blocks (%) 1 1 2 63.01 1 1 3 62.60 1 1 4 63.41 2 2 2 61.38 2 2 3 61.79 2 2 4 62.20 3 3 2 58.54 3 3 3 61.79 3 3 4 58.55 4 4 2 58.94 4 4 3 61.38 4 4 4 60.57 Discussions and Limitations There are still some limitations regarding the elicited micro-expressionsterm and this database: First, the labeling for these spontaneous micro-expressionsterm is not very satisfactory. The materials for eliciting micro-expressionsterm are video episodes which are complex stimuli and can have different meanings to different people. For example, the scene of chewing worms is not always disgusting—some participants actually reported “funny” or “interesting”. Categorizing the micro-expressionsterm simply based on AUs (especially for AU combinations that haven't been defined) requires further in-depth studies. Participants are required to control and suppress their facial expressions, which often makes spontaneous micro-expressionsterm appear partial and sometimes with only a single AU (also see Porter, 2008; YanpersName et al., 2013). Even though we considered the AU combinations, the content of the video episodes and the participants' report, the emotion labels for these micro-expressionsterm remain debatable. Second, the micro-expressionsterm in this database are elicited under one specific lab situation and may not cover the micro-expressionsterm elicited under other situations. Micro-expressionsterm that occur when people are under an police interrogation or lying to their boss, may be different from those we observe in a laboratory situation. Up to now, very few studies have investigated the micro-expressionsterm elicited in different situations. Future studies should be conducted to explore the variations of micro-expressionsterm in different situations. Third, it's unclear which method is most suitable for classifying micro-expressionsterm. The micro-expressionsterm have the following characteristics which are different from the ordinary facial expressions in the following ways: (1) low-intensity facial movements and (2) partial facial expressions (fragments). Therefore, previous methods that were suitable for classifying ordinary facial expressions may not work well for micro-expressionsterm. We provided this database and a baseline evaluation, so future research may compare with our initial results. Fourth, the participants came from a limited range of age. Those who participated in the elicitation section are young people. Most of them are university students. It would be better if the database contain samples from models at various ages. Conclusions This paper reviews the previously developed micro-expressionterm databases and presents a new and improved one. This new spontaneous micro-expressionterm database, CASME IIterm, includes 247 micro-expressionterm samples which is larger than previous micro-expressionterm databases. The micro-expressionsterm were elicited in a well-controlled laboratory environment and they were recorded by a high-speed camera (200 fps) so the temporal resolution is much higher than previous spontaneous micro-expressionterm databases. For the spatial resolution, the size of the faces is larger than previous spontaneous micro-expressionterm samples; as larger scale of images helps to reveal tiny changes, which would benefit feature extraction and futher better classification. The recordings have proper illumination without flicking light and with reduced highlight regions on the face. These samples were coded with onset and offset frames as well as AUs and emotion labels. We also reported the baseline results of evaluation using a combination of LBP-TOPterm and SVM. Micro-expressionterm analysis is a new research area. Starting from a controlled laboratory environment can make the database well focused on the subtle expressions and rule out irrelevant factors. We provide the CASME IIterm database as a benchmark for the future exploration of potential micro-expressionterm analysis algorithms that are suitable for detecting subtle facial muscle movements for emotional states recognition. In the future, we will extend our research to a more natural environment for expression and micro-expressionterm analysis in natural conversation and interaction. The CASME IIterm database is online public available now for testing (see http://fu.psych.ac.cn/CASME/casme2-en.php for details). Funding Statement This work is partly supported by the National Natural Science Foundation of China (61075042, 61322206, 61375009, 61379095); 973 Program (2011CB302201), China Postdoctoral Science Foundation (2012M580428); NCET-11-0273; GZ and XL were supported by Academy of Finland and Infotech Oulu. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.