Research Article  Open Access
Juan Cheng, Fulin Wei, Yu Liu, Chang Li, Qiang Chen, Xun Chen, "Chinese Sign Language Recognition Based on DTWDistanceMapping Features", Mathematical Problems in Engineering, vol. 2020, Article ID 8953670, 13 pages, 2020. https://doi.org/10.1155/2020/8953670
Chinese Sign Language Recognition Based on DTWDistanceMapping Features
Abstract
Sign language is an important communication tool between the deaf and the external world. As the number of the Chinese deaf accounts for 15% of the world, it is highly urgent to develop a Chinese sign language recognition (CSLR) system. Recently, a novel phonology and radicalcoded CSL, taking advantages of a limited and constant number of coded gestures, has been preliminarily verified to be feasible for practical CSLR systems. The keynote of this version of CSL is that the same coded gesture performed in different orientations has different meanings. In this paper, we mainly propose a novel twostage feature representation method to effectively characterize the CSL gestures. First, an orientationsensitive feature is extracted regarding the distances between the palm center and the key points of the hand contour. Second, the extracted features are transformed by a dynamic time warping (DTW) based feature mapping approach for better representation. Experimental results demonstrate the effectiveness of the proposed feature extraction and mapping approaches. The averaged classification accuracy of all the 39 types of CSL gestures acquired from 11 subjects exceeds 93% for all the adopted classifiers, achieving significant improvement compared to the scheme without DTWdistancemapping.
1. Introduction
Sign language (SL), a structured form of hand gestures, is the combination of various signs, hand movements, or body and facial expressions to deliver information. The motivation of SL recognition (SLR) is to provide a translation system to bridge the communication between the deaf and the healthy hearing society. From the literatures, cameras, data gloves, accelerometers (ACCs), and surface electromyography (sEMG) sensors are some common acquisition devices to capture gesture signals [1–3]. Currently, many SLs have been studied worldwide for the purpose of recognition, typically as American SLs, Australian SLs, Arabic SLs, Greek SLs, Korean SLs, and Chinese SLs (CSLs) [4–7]. According to the global estimates on prevalence of hearing loss provided by WHO, about 360 million people of such kind are in the world, among whom approximately onefifth are Chinese [8]. Thereby, it is urgent to develop a practical and effective CSL recognition (CSLR) framework.
In the past decades, researchers have proposed various solutions to improve the performance of CSLR [9, 10]. For instance, Fang et al. employed datagloves as input devices and designed a CSLR system based on a hierarchical decision tree, which could recognize 5113isolatedsign vocabulary with accuracy rates of 91.6% and 83.7% for the registered and unregistered sets, respectively [2]. Li et al. [3] proposed an automatic componentlevel CSLR framework using the combined information from both ACC and sEMG signals. The overall classification results were 96.5% and 86.7% for a vocabulary of 12 0 signs and for 200 sentences, respectively. Later, Ma et al. employed a probabilistic model, hidden conditional random field (HCRF), to recognize CSLs based on sEMG and ACC signals. The average recognition rate was 91.51% for 120 highfrequency CSL words [11]. In 2015, Zhang et al. proposed a new system for both isolated and continuous CSLR based on video data. For isolated CSLR, the histogram of oriented displacement (HOD) was used to describe the trajectories, while the multiSVM was adopted for classification. As for continuous CSLR, a dynamic programming method with warping templates obtained by DTW was adopted. The averaged accuracies of 450 phrases and 180 sentences were 88.0% and 85.2%, better than those obtained by HMMs, as well as CRF and its advanced techniques [12]. By considering the typical posture and motion simultaneously, Wang et al. proposed a sparse observation representation approach for CSLR, yielding better performance than traditional DTW and HMMs [13]. Besides the CSLR systems with traditional machine learning frameworks, there are a lot of studies employing advanced deep learning techniques [9, 14–16]. For instance, Yang and Zhu proposed a CSLR system based on convolutional neural networks using video data [15]. In [9], Huang et al. delivered a novel sequencetosequence learning method based on keyframe centered clips (KCCs) for isolated CSLR. The CSL sequence learning was realized by establishing an encoderdecoder using a long shortterm memory (LSTM) network, achieving an average accuracy of 91.18% for 310 CSL words. Xiao et al. adopted a dual LSTM combined with a couple conditional HMM to fuse hand and skeleton sequence information to recognize continuous CSLs using RGBD data [16].
Currently, the sign gestures employed by CSLR systems generally refer to the handbook edited by China Association of the Deaf [17], which has about 5000 normalized sign gestures. However, since the number of frequently used Chinese characters is about 3500, the number of frequently used words is far more than tens of thousands. It is obvious that these gestures are not enough for practical applications. Besides, most of these sign gestures are symbolic and consist of complex components, which are sometimes hard to be standardized due to regional differences and personal habits. To overcome these limitations, a completely new version of phonology and radicalcoded CSL execution has been introduced by the same association. In this version, each Chinese character can be expressed by executing the coded gestures twice using both hands. The first execution of phonologycoded gesture is near the mouth or the chest, representing Mandarin Pinyin. The second execution of radicalcoded gesture is near the waist, representing the fore and the end radicals. In this way, almost every Chinese character can be composed of the only 13 basic gesture shapes [17, 18], with each having 3 orientations (totally 39 gestures), shown in Figure 1. The defined 13 gesture shapes are shown in Figure 1(a) and named after four capital letters. The 3 orientations are shown in Figure 1(b) and abbreviated as U, H, and D, corresponding to vertical upwards, horizontal, and vertical downwards, respectively.
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The coded rules to express a Chinese character can be seen from Table 1 in [18]. Taking the Chinese character “huang” (yellow) for instance, the pronunciation is formed by the fast sounding of basic initial consonant code “h” (right hand, “ASLB” with a horizontal orientation) and the basic final consonant code “uang” (left hand, “ASLW” with a horizontal orientation). It is expressed by the first execution of phonologycoded gestures using both hands. However, common characters “huang” (empire), “huang” (yellow), and “huang” (lie) share the same pronunciation “huang” but can be distinguished from the fore and the end radicals, as seen in [18]. These radicals are expressed by the second execution of radicalcoded gestures using both hands. Thereby, two executions of coded gestures can determine one Chinese character. By representing Chinese characters onebyone based on the defined coded gestures, a word or a complete sentence can be formed to express a specific meaning. It can be seen from Figure 1 that all the coded gestures are somewhat static and will not be influenced when the same character is expressed in different words or different sentences. Meanwhile, the number of the coded gestures is limited and constant, which are relatively simply designed so that the standardization of them becomes much easier. Consequently, the most important thing is to recognize the 13 hand shapes with 3 different orientations for this version of CSL including relatively static gestures without the careful consideration of movement epenthesis (ME) problem.

This paper aims to develop an orientationsensitive and robust feature to represent the above CSL gestures that are executed with different number of stretchingout fingers and different orientations. The main contributions of this paper can be summarized into the following two aspects:(1)We propose an orientationsensitive feature extraction approach based on the distances between the palm center and the key points of the hand contour for CSLR. The key points are extracted by considering the information of stretchingout fingers. The proposed features are capable of representing different orientations of the same gesture shape, as well as characterizing the spatial distribution of stretchingout fingers with the same number but different hand shapes.(2)We introduce a DTWbased feature mapping method to further improve the recognition accuracy. The feature mapping method calculates the DTW distances between the current sample and a set of reference samples covering all categories of gestures, aiming to improve the effectiveness of feature representation. To the best of our knowledge, it is the first time that the DTW algorithm is employed for feature mapping in visual recognition problems.
The rest of this paper is structured as follows. Section 2 introduces some related works. In Section 3, we present the proposed CSLR framework, mainly including the extraction of orientationsensitive features and the feature mapping based on DTW distances. The detailed experimental results and discussions are provided in Section 4. Finally, Section 5 concludes the paper.
2. Related Work
2.1. OrientationSensitive Feature Extraction
Since cameras are simple, noncontact, convenient, and natural devices to capture gesture videos, visionbased approaches have drawn much attention in the field of CSLR [4, 6, 9]. In order to well recognize gestures, feature extraction is an important procedure, which sometimes should consider the rotation or orientation information of gestures. To address this problem, researchers have put forward some solutions. The first type is to develop orientationsensitive descriptors. For instance, the histogram of gradient (HOG) is a typical rotationdependent indicator to describe the appearance and the shape of a local object within an image using the distribution of intensity gradients [19]. Wang et al. proposed a novel sparse observation representation approach to convert the highdimensional HOG features into lowdimensional sparse vectors while obtaining better classification performance [13]. The other type is to evaluate the angles between the actual orientation and the designated orientation of the gesture. For instance, Huang et al. used Gaborfilterbased method to estimate the angles between the gesture orientations and the designated orientation, which later helped to correct the hand pose into the upright orientation [20]. Priyal and Bora utilized the geometry information of gestures to correct gesture orientation by calculating the histogram of palm length to palm width ratios [21].
2.2. Dynamic Time Warping
DTW, proposed by Itakura, is an algorithm to measure the similarity of two distorted trajectories, which overcomes the scale invariance of two models by aligning them in the similarity matrix [22]. If the lengths of two time series that need to compare similarities are not equal, the time axis length of one sequence or both sequences needs to be “warped” for obtaining a better alignment result [23]. The goal of DTW is to find an optimal match path between these two unequal sequences by using a dynamic programming approach, allowing shifting of the time axis. This matching process requires compensation for length differences and the nonlinear properties of length differences in two sequences should be taken into account [24]. Thanks to its small training data requirement and high accuracy, DTW has been widely used in both sequential and nonsequential models. Readers can refer to the survey on DTW for detailed information [25]. Currently, more and more advanced DTW techniques, such as VQDTW [26], weightedDTW [27], and SoftDTW [28], have been developed. They have been widely used in biometric identification [29], static and dynamic gesture recognition [30, 31], medical examination [32], etc. Most of the DTWbased studies calculated the similarity of two sequences for classification directly according to the minimum cost or using cluster algorithms. In this paper, the DTWbased distance is employed for feature mapping to improve the effectiveness of feature representation.
3. The Proposed CSLR Method
The flow chart of our Chinese sign language recognition framework based on DTWdistancemapping features is illustrated in Figure 2. First, gesture videos are acquired and framed to images. Second, each frame of image is transformed to hue, saturation, and value (HSV) color space for hand segmentation [33] and the segmented image is resized. Third, a palm center is localized based on hand boundary contour points and a constant number of key points are determined. The orientationsensitive features are then extracted based on the distances between the palm center and all the key points. Afterwards, the orientationsensitive feature is mapped into DTW distances via DTW algorithm between a certain sample and reference samples. Finally, the DTWdistancemapping features are sent to classifiers for training and testing.
3.1. Hand Segmentation
Different hand segmentation approaches have been available in literature to separate the hand region from the background [33, 34]. Among them, the colorbased method is common, which is a distinctive cue of hands and is invariant to scale and rotation [35]. Before performing this method, the framed images from acquired gesture videos in RGB color space are first converted to the HSV color space. Both thresholds ( and ) of hue and saturation are defined to segment the hand region. In this paper, if the hue pixels from a region are smaller than and the saturation pixels from the same region are larger than , these pixels will be determined within the hand region. and are experimentally set to 0.1 and 0.2.
Then, Otsu’s method is utilized to better separate the skin hand region from the nonskin background region, with the criterion that the intraclass variance between the two regions is minimal [36]. Morphological dilateerode operations are then adopted to eliminate the noise interference. Subsequently, a guided filter, namely, edgeaware image filtering, is performed to get a smooth hand edge [37]. Scale normalization is utilized to resize the cropped hand regions to a constant size of 256 × 256. The results of hand segmentation are shown in Figure 3. Figure 3(a) shows an image of gesture “EXPM,” and Figure 3(b) shows the result of skincolorbased hand segmentation. Figures 3(c) and 3(d) show the handcropped region after guided filtering and the resized handcropped region, respectively.
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3.2. Feature Extraction
3.2.1. Palm Center Localization
After the hand segmentation, the hand boundary contour is tracked by the 8neighborhood contour tracing algorithm, resulting in a sequence of hand contour points for each image [38]. For the purpose of determining the number of stretchingout fingers, the palm circle with a proper radius value is adopted to eliminate the palm region. To achieve this goal, the palm center is first localized according to the hand boundary contour points. The detailed procedures are as follows:(1)First, the centroid coordinate of the segmented hand region in the resized image is calculated. The contour point at the bottom of the centroid is assigned as the initial point. Then, the adjacent point in the clockwise direction will be assigned as the second point. In this way, the sequences of the hand contour points are generated.(2)Second, the Euclidean distance between each contour point in the sequence and the centroid is calculated, and its minimums, representing the finger roots, are detected using peak detection algorithm [39].(3)Third, by applying the leastsquare method to the detected finger roots, a fitting circle is obtained. The palm center is defined as the center of the fitting circle. Let denote the radius of the circle. In order to cover all the stretchingout fingers without submerging them, an enlarged circle with the radius is adopted. After eliminating the palm by the enlarged circle (setting all the pixels within the circle to zeros), the number of the connected regions corresponding to stretchingout fingers is calculated.
Figure 4 shows the detected finger roots and the corresponding fitting circle. It can be seen from Figures 4(a) and 4(b) that the distance minimums indicate the locations of finger roots, marked as blue stars. The center of the fitting circle (denoted as the red rectangle) and the isolated fingers after the palm elimination are shown in Figure 4(c).
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3.2.2. Key Point Determination
After palm elimination, the hand boundary contour points are redetected to form a new sequence and the distance between each new contour point and the palm center is recalculated. The fingertips are determined as the locations corresponding to distance maximums, while finger roots are determined by the threshold (). Figure 5 shows the detected fingertips and finger roots of gesture “EXPM,” where Figure 5(a) is in the form of distance sequence, while Figure 5(b) is in the form of hand. It can be clearly seen that the maximums (red stars) indicate the fingertips, while the minimums (blue stars) can represent the finger roots. Compared to the minimums in Figure 4(a) where there exist inaccurate or missing finger roots, the detection of finger roots based on our proposed palm elimination technique is more accurate, shown in Figure 4(b) accordingly. Similar conclusions can be found in [30, 40].
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In this paper, five key points are selected to represent each stretchingout finger. One point is the fingertip, two other points are the finger roots, and the remaining two points are the midpoints that relate to knuckles. The midpoints are determined as the middle of the horizontal distances between the fingertip and ambilateral adjacent finger roots. The detected midpoints can be seen as the pink stars in Figure 5(b).
Since at most five fingers can stretch out simultaneously, the maximum number of the key points is 25. With the aim of making all the gestures be classified at once while reducing the computation complexity, the number of all the key points is set to be a constant , where is not smaller than 25. Suppose that there are stretchingout fingers detected from the resized handsegmented binary image ; a total of points can be formed orderly to represent all the stretchingout fingers. The key points consist of two parts: one part includes the points representing the stretchingout fingers and the other part includes all the hand boundary contour points inside the interval from the location of the last finger root clockwise to that of the first finger root, which is equally normalized to boundary contour points.
Figure 5 gives an example of 40 key points of gesture “EXPM” with 5 stretchingout fingers. So far, 25 key points have been determined. In order to form the total 40 hand boundary contour points, the remaining 15 points are uniformly sampled from the interval clockwise from the rightmost finger root to the leftmost finger root, shown as the green stars in Figure 5(b).
There is a particular gesture “HDGP” where none of the finger are stretching out. In this case, Harris corner detection algorithm [41] is adopted to derive the corresponding hand boundary contour points, and all the points are normalized to points.
3.2.3. Distance Calculation
All the Euclidean distances between the key points and the palm center are calculated as the orientationsensitive features. Specifically, the Euclidean distance between the kth key point and the palm center is calculated as
3.3. DTWDistanceBased Feature Mapping
To further improve the effectiveness of feature representation, the original orientationsensitive feature of each gesture sample is then mapped into the corresponding dynamic time warping (DTW) distance between it and the reference samples. The procedures are as follows: samples of each type of gesture are selected as the reference samples. For 39 types of coded gestures, the rth feature vector can be denoted as . The original ith training feature can be mapped to the new feature according towhere . is the total number of training gesture samples. Similarly, the original testing feature can be mapped to the new feature according to Equation (2), too. The number of the new feature dimensions only depends on the total number of reference samples. Due to the inherent property of DTW, the original orientationsensitive features are transformed to the new feature space with higher similarity [42]. Besides, since the new features consist of the DTW distances between the sample and all types of gestures, the relationship between each two types of gestures is considered to strengthen the separability of features.
After feature mapping, the new training and testing features will be sent to the classifiers. The effective classifiers can be linear discriminant analysis (LDA) [18], KNearest Neighbors (KNN) [43], support vector machine (SVM) [44], and hidden Markov models [10]. Before sending the features into classifiers, a feature dimension reduction technique, uncorrelated linear discriminant analysis (ULDA), is performed to reduce the feature dimension into , where is the number of gesture types [45]. On the one hand, Fukunaga proposed the optimal dimension of feature space as for class problem [46]. On the other hand, the optimizing criterion in LDA is the ratio of betweenclass scatter to the withinclass scatter, and, for any class problem, nonzero eigenvalues will be obtained [45]. As it is known that ULDA is an extension of LDA by adding some constraints into the optimization objective of LDA, the feature vectors extracted by ULDA will contain minimum redundancy [47]. Thereby, the dimension of the reduced features after ULDA is for class problem.
4. Experiment and Results
4.1. Data Acquisition
A builtin camera from Vivo X7 (Vivo Inc., Guangzhou, China) was employed to collect gesture videos. It was attached on a laptop, with a distance of 0.2 m away from the subject. All the videos were recorded with a frame rate of 30 frames per second (fps) and a resolution of . With the approval of the Ethics Review Committee of Hefei University of Technology, 11 informed subjects (3 females and 8 males, with the average age of 23.64 ± 1.03 years) were recruited in the experiment. Every subject performed all the 39 types of gestures sequentially with each lasting for about 5 seconds, resulting in a total number of 39 videos (all the videos are publicly available and can be obtained from Baidu Cloud Engine 1). Since this paper aims to propose a robust and orientationdependent feature to classify CSL gestures, the environmental settings of data acquisition were relatively simple, regardless of complex background, intensive illumination variations, different colors of skin, and so forth. Some effective solutions addressing these problems can be found in [21, 44]. Each acquired video was framed to images on MATLAB platform and downsampled to 10 fps. Finally, for each gesture, 50 images were obtained in order to meet the sample requirement of classifiers and provide samples with different illumination variations and tilted angles, resulting in 21450 samples in total (50 frames/gesture × 39 gestures/subject × 11 subjects = 21450 frames).
4.2. Classification Results
Three commonly used classifiers, linear discriminant analysis (LDA), support vector machine (SVM), and KNearest Neighbors (KNN) [43, 48], were employed to verify the effectiveness of the DTWdistancemapping features.
4.2.1. With DTWDistanceMapping versus without DTWDistanceMapping
Two classification schemes were designed to demonstrate the feasibility of our proposed mapping features. They were classifying all 39 types of gestures with and without DTWdistancemapping, termed as Scheme I and Scheme II, respectively. For Scheme I, once the reference samples were determined, the samples from the remaining 10 subjects were further divided into training samples and testing samples by applying the leaveonesubjectout strategy again; namely, each one of the 10 subjects took turns to be the testing subject, while the samples of the other 9 subjects were used for training. As for Scheme II, the above twolevel leaveonesubjectout strategy for training and testing was exactly the same as Scheme I for fair comparison. That is to say, a reference subject was also selected in each round (at the first level) but was just removed from the experiment instead of being used for DTW feature mapping.
The averaged classification rates of our proposed CSLR framework among all the 11 subjects are illustrated in Figure 6. It can be seen clearly from Figure 6 that the results of DTWmappingdistance features are all higher than 93%. Specifically, the results are 93.20 0.80%, 99.03 0.19%, and 94.92 0.87% for LDA, SVM, and KNN, respectively, while those of without DTW scheme are 84.33 0.97%, 97.16 0.19%, and 87.78 0.44%, respectively (in the form of Mean Std). The averaged classification rates of Scheme I are improved by 8.87%, 1.87%, and 7.14% compared to Scheme II, respectively.
A oneway ANOVA test was conducted on the results in Figure 6 to explain the difference between the two schemes from the statistical view [48, 49]. The values derived from the ANOVA analysis are listed in Table 1. The maximum value among all the classifiers is 2.48e13 (), indicating that there is a significant difference between these two schemes.
Furthermore, we take the results based on LDA as an example to show the specific classification results of each subject in Figure 7. The blue ones represent the results of Scheme I, while the yellow ones represent those of Scheme II. It can be seen that DTWdistancemapping features can almost improve corresponding classification accuracy for each subject, with the improvement of at least 6.68%. The best classification accuracy improvement can reach 10.35%, achieved from subject S10.
Taking the results of subject S6 as an example, a specific classification confusion matrix of 39 types of gestures is illustrated in Figure 8. Figure 8(a) represents the results of Scheme I with KNN, and the corresponding results of Scheme II are shown in Figure 8(b). The horizontal and vertical coordinates represent predicted class and actual class, and the values of the axes represent the gesture types. The first 13 values represent all types of the downward gestures and then the horizontal and upward ones. It can be seen from Figure 8(b) that most types of gestures are well classified, and some of them can be 100% classified, such as “DASLW” and “UPINT.” It can be clearly seen that the misclassified gesture in Scheme I is significantly reduced, especially for line 6 (gesture “DEXIF”), line 23 (gesture “HEXTF”), and line 33 (gesture “UEXTF”). These results verify that the separability ability can be improved by DTWdistancemapping features compared to the original features.
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4.2.2. DTW Matching versus DTWDistanceMapping
In order to show the better performance of DTWdistancemapping compared to that of DTW matching, where the latter one means adopting DTW as a template matching for classification, we first give the overall classification accuracy of 11 subjects using DTW matching and DTW mapping followed by classifiers, listed in Table 2. The accuracy of using DTW matching for all 39 types of gestures is only 29.30%, while that of using DTW mapping followed by classifier is at least 93.20%, with an improvement of at least of 63.90%. Meanwhile, all 39 types of gestures can be divided into subsets according to the number of stretchingout fingers. The numbers of gesture types of each subset are 3, 9, 12, 9, 3, and 3. The overall classification accuracy of each subset is also shown in Table 2, with corresponding to stretchingout fingers. All the results demonstrate that the strategy of DTW mapping followed by classifiers is superior to that of traditional DTW matching alone. It can be seen that the number of gesture types of subset is 9, 3 fewer than that of subset. However, the overall classification accuracy of subset is lower than that of subset during every case. It can be indicated that when the number of the stretchingout finger is fewer, the DTW distance between each two feature sequences will be much similar. By means of DTW mapping, all the features are transformed into minimum cumulative distance of warp path while considering the differences between each two samples [30]. Thereby, the DTW mapping can enhance the separation ability of features to improve the classification accuracy. However, as for and subsets, although there are only 3 types of gestures in each subset, the traditional DTW matching method is unable to recognize them well, with the classification accuracy of about 74%. It indicates that the traditional DTW matching method cannot well distinguish the same gesture but with different orientations.
 
stands for the number () of the stretchingout fingers. Here, ranges from 0 to 5. “All” means all 39 types of gestures. 
To further demonstrate the feasibility of DTW mapping strategy, the classification confusion matrices for all the 9 types of gestures (subject 1 was designated as the reference subject) derived from DTW mapping followed by KNN and DTW matching are shown in Figure 9, with Figure 9(a) representing the confusion matrix using DTW matching and Figure 9(b) representing results using the DTW mapping. The parameters of the KNN were set to 100 neighbors, Euclidean distance, and equal distance weight, respectively. According to Figure 9(a), except for the accuracies of 66% for gesture “DEXIF” and 59% for gesture “HEXIF,” the recognition rates of the other 7 gestures are all less than 50% when using DTW as template matching. However, when adopting the strategy of DTW mapping, the average recognition accuracy is significantly improved, with the lowest recognition rate of 81% for the gesture “DEXTF.” The overall recognition accuracy is 89.50%, with an improvement rate of 62.06% compared to that using DTW matching.
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The reasons why the performance of DTW mapping followed by classifiers is superior to that of DTW matching can also be revealed from the feature representation perspective. Taking the 9 types of gestures for instance, Figure 10 shows the original orientationsensitive features, while Figure 11 shows the corresponding DTWdistancemapping features. It can be seen from Figure 10 that, even with the same number of stretchingout fingers, the corresponding features are different with the same orientation (comparison of each 3 horizontal subfigures). For the same gesture with different orientations, the corresponding features are also different (comparison of each 3 vertical subfigures). However, when only one finger is stretching out, the original feature waveforms are similar but with a shifting of the time axis, which cannot be well distinguished using the DTW matching. The reason is that the DTW distance between these two feature sequences with similar waveform but time shifting is small.
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It can be seen from Figure 11 that the DTWdistancemapping features of all types of gestures in subset are separable. Specifically, the DTWdistancemapping feature of “UEXLF” is quite different from that of “HEXIF,” which indicates that, by considering the relationship between each two types of gesture via DTW distance, the DTWdistancemapping features are more separable.
4.3. Discussion
The above results clearly demonstrate that (1) the proposed feature extraction approach based on DTWdistancemapping is effective for CSLR and (2) the DTWbased feature mapping strategy can further improve the recognition accuracy. By using DTW mapping, the features are transformed into minimal cumulative distance of warp path to enhance the feature representation ability, which can overcome the differences between subjects, such as gesture execution habits or different sizes of the palm. To further analyze the results, we compared them with other related studies. In [50], Liu et al. designed a hierarchical strategy based on the number of fingers to solve the “largecategory” problem of SLs, and HOG features were used. The results of 25 popular static gestures were 78.9% for training gestures in a fixed orientation and 99.9% for training gestures in multiple orientations. Nevertheless, the results of 39 types of gestures in our study can also reach 99.03% using DTWdistancemapping combined with SVM. Besides, the maximum dimension of the orientationsensitive features is only 40, which is far smaller than that of the HOG features. Subsequently, in 2016, Chen et al. proposed locating palm center, fingertips, and finger roots, as well as rootcenterangle features using finger length weighted Mahalanobis distance. The average accuracy of the 15 selfcollected gestures with 9 rotations was 94.67%, which benefitted from the usage of the supernumerary depth data from Kinect sensor and the robustness to illumination variations [51]. Ren et al. proposed a novel FingerEarth Mover's Distance (FEMD) method to measure the dissimilarity between hand shapes, which helped to achieve an average accuracy of 93.2% for 10 gestures. The FEMD was robust to hand articulations, distortions, and orientation or scale changes and could even handle challenging situations including cluttered backgrounds and lighting conditions [40, 52]. However, in our study, the same gesture executed with different orientations is treated as a different gesture, where the FEMD method is suboptimal. Meanwhile, the classification performance of using DTWdistancemapping features has been proved to be better than that using DTW as template matching. In addition, the timeconsumption complexity of our proposed DTW is , while that of EMD algorithm is cubic, specifically [53]. The mean running times of a hand recognition procedure were reported as 4.0012 seconds and 0.0750 seconds for nearconvex decomposition followed by FEMD and the thresholding decomposition followed by FEMD, respectively [40]. As for our DTWdistancemapping method, the mean running time of a hand recognition procedure was 0.07188 seconds (MATLAB 2015b, i57500 CPU, 8G RAM).
It is noted that a drawback of our proposed CSLR system is the skincolorbased hand segmentation approach, which has restrictions such as illumination invariance and relative simple background, although this step is not the focus of this work. In the future, we will develop a more robust hand segmentation method to improve the practicability of the proposed CSLR system. The other drawback of our proposed CSLR framework lies in determining the radius of the palm fitting circle, which is important to determine the number of stretchingout fingers. Due to physiological anatomy and gesture execution habit differences between subjects, the radius of the palm fitting circle currently cannot be constant among all the subjects. In the future, we will adopt some adaptive methods to solve this problem. The other problem is that there is a restriction of finger occlusion. If the stretchingout fingers are occluded but the maximum distance between the distance contour point and the palm center is larger than the radius R, the corresponding fingertips can still be detected. However, if the maximum distance is not larger than the radius due to severe occlusion, the corresponding fingertips cannot be detected by our proposed method. In this case, gestures based on RGBD data and FEMD method might provide valuable information that can handle severe occlusion problem [40]. Besides, with the development of deep learning technique, deeplearningbased CSLR system will also be further explored.
5. Conclusions
In this paper, we have proposed a novel twostage feature representation method for Chinese sign language recognition (CSLR). The target is to classify 39 types of static CSL gestures, consisting of 13 types of basic hand shapes with each having 3 orientations. An orientationsensitive feature extraction approach is presented based on distances between the palm center and the key points of the hand contour. Besides, a dynamic time warping (DTW) based feature mapping approach is proposed to further improve the recognition accuracy. Experimental results demonstrate the effectiveness of the proposed feature representation approach. This study will facilitate the practical progress of CSLR systems.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
The authors would like to thank all the volunteers for their contributions to the experiments. This work was supported by the National Key R&D Program of China (Grant no. 2017YFB1002802), the National Natural Science Foundation of China (Grant nos. 61922075, 61701160, and 41901350), the Provincial Natural Science Foundation of Anhui (Grant no. 1808085QF186), and the Fundamental Research Funds for the Central Universities (Grant nos. JZ2020HGPA0111 and JZ2019HGBZ0151).
References
 M. J. Cheok, Z. Omar, and M. H. Jaward, “A review of hand gesture and sign language recognition techniques,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 1, pp. 131–153, 2019. View at: Publisher Site  Google Scholar
 G. Fang, W. Gao, and D. Zhao, “Large vocabulary sign language recognition based on fuzzy decision trees,” IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans, vol. 34, no. 3, pp. 305–314, 2003. View at: Publisher Site  Google Scholar
 Y. Li, X. Chen, X. Zhang, K. Wang, and Z. J. Wang, “A signcomponentbased framework for Chinese sign language recognition using accelerometer and semg data,” IEEE Transactions on BioMedical Engineering, vol. 59, no. 10, pp. 2695–2704, 2012. View at: Publisher Site  Google Scholar
 M. Mohandes, M. Deriche, and J. Liu, “Imagebased and sensorbased approaches to Arabic sign language recognition,” IEEE Transactions on HumanMachine Systems, vol. 44, no. 4, pp. 551–557, 2014. View at: Publisher Site  Google Scholar
 E.J. Holden, G. Lee, and R. Owens, “Australian sign language recognition,” Machine Vision and Applications, vol. 16, no. 5, pp. 312–320, 2005. View at: Publisher Site  Google Scholar
 T. Starner, J. Weaver, and A. Pentland, “Realtime american sign language recognition using desk and wearable computer based video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1371–1375, 1998. View at: Publisher Site  Google Scholar
 J. Feng, G. Wen, H. Yao, D. Zhao, and X. Chen, “Synthetic data generation technique in signerindependent sign language recognition,” Pattern Recognition Letters, vol. 30, no. 5, pp. 513–524, 2009. View at: Google Scholar
 http://www.who.int/pbd/deafness/WHO_GE_HL.pdf.
 S. Huang, C. Mao, J. Tao, and Z. Ye, “A novel Chinese sign language recognition method based on keyframecentered clips,” IEEE Signal Processing Letters, vol. 25, no. 3, pp. 442–446, 2018. View at: Publisher Site  Google Scholar
 W. Yang, J. Tao, and Z. Ye, “Continuous sign language recognition using level building based on fast hidden markov model,” Pattern Recognition Letters, vol. 78, pp. 28–35, 2016. View at: Publisher Site  Google Scholar
 D. Ma, X. Chen, Y. Li, J. Cheng, and Y. Ma, “Surface electromyography and acceleration based sign language recognition using hidden conditional random fields,” in Proceedings of the 2012 IEEEEMBS Conference on Biomedical Engineering and Sciences, pp. 535–540, Langkawi, Malaysia, December 2012. View at: Publisher Site  Google Scholar
 J. Zhang, W. Zhou, and H. Li, “A new system for Chinese sign language recognition,” in Proceedings of the 2015 IEEE China summit and international conference on signal and information processing (ChinaSIP), pp. 534–538, Chengdu, China, July 2015. View at: Publisher Site  Google Scholar
 H. Wang, X. Chai, and X. Chen, “Sparse observation (SO) alignment for sign language recognition,” Neurocomputing, vol. 175, pp. 674–685, 2016. View at: Publisher Site  Google Scholar
 P. Molchanov, X. Yang, S. Gupta, K. Kim, S. Tyree, and J. Kautz, “Online detection and classification of dynamic hand gestures with recurrent 3D convolutional neural networks,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4207–4215, Las Vegas, NV, USA, June 2016. View at: Publisher Site  Google Scholar
 S. Yang and Q. Zhu, “Videobased chinese sign language recognition using convolutional neural network,” in Proceedings of the 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), pp. 929–934, Guangzhou, China, May 2017. View at: Publisher Site  Google Scholar
 Q. Xiao, M. Qin, P. Guo, and Y. Zhao, “Multimodal fusion based on lstm and a couple conditional hidden markov model for Chinese sign language recognition,” IEEE Access, vol. 7, pp. 112 258–112 268, 2019. View at: Publisher Site  Google Scholar
 China Association of the Deaf, Chinese Sign Language (Revised Edition), Hua Xia Press, Beijing, China, 2003, in Chinese.
 J. Cheng, X. Chen, A. Liu, and H. Peng, “A novel phonology and radicalcoded Chinese sign language recognition framework using accelerometer and surface electromyography sensors,” Sensors, vol. 15, no. 9, pp. 23 303–324, 2015. View at: Publisher Site  Google Scholar
 N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), pp. 886–893, San Diego, CA, USA, June 2005. View at: Publisher Site  Google Scholar
 D.Y. Huang, W.C. Hu, and S.H. Chang, “Gabor filterbased handpose angle estimation for hand gesture recognition under varying illumination,” Expert Systems with Applications, vol. 38, no. 5, pp. 6031–6042, 2011. View at: Publisher Site  Google Scholar
 S. P. Priyal and P. K. Bora, “A robust static hand gesture recognition system using geometry based normalizations and krawtchouk moments,” Pattern Recognition, vol. 46, no. 8, pp. 2202–2219, 2013. View at: Publisher Site  Google Scholar
 F. Itakura, “Minimum prediction residual principle applied to speech recognition,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 23, no. 1, pp. 67–72, 1975. View at: Publisher Site  Google Scholar
 E. J. Keogh and M. J. Pazzani, “Derivative dynamic time warping,” in Proceedings of the 2001 SIAM International Conference on Data Mining, pp. 1–11, Chicago, IL, USA, April 2001. View at: Publisher Site  Google Scholar
 D. J. Berndt and J. Clifford, “Using Dynamic Time Warping to Find Patterns in Time Series,”, In KDD Workshop, Seattle, WA, USA, 1994.
 C.Y. Kao and C.S. Fahn, “A humanmachine interaction technique: hand gesture recognition based on hidden markov models with trajectory of hand motion,” Procedia Engineering, vol. 15, pp. 3739–3743, 2011. View at: Publisher Site  Google Scholar
 M. Faundez Zanuy, “Online signature recognition based on VQDTW,” Pattern Recognition, vol. 40, no. 3, pp. 981–992, 2007. View at: Publisher Site  Google Scholar
 Y.S. Jeong, M. K. Jeong, and O. A. Omitaomu, “Weighted dynamic time warping for time series classification,” Pattern Recognition, vol. 44, no. 9, pp. 2231–2240, 2011. View at: Publisher Site  Google Scholar
 M. Cuturi and M. Blondel, “SoftDTW: a differentiable loss function for timeseries,” in Proceedings of the 34th International Conference on Machine Learning, pp. 894–903, Sydney, Australia, August, 2017. View at: Google Scholar
 L. Yang, J. Shen, S. Bao, and S. Wei, “Biometric identification method for ECG based on the piecewise linear representation (PLR) and dynamic time warping (DTW),” Journal of Biomedical Engineering, vol. 30, no. 5, pp. 976–981, 2013. View at: Google Scholar
 C. Hong, Z. Dai, Z. Liu, and Z. Yang, “An imagetoclass dynamic time warping approach for both 3d static and trajectory hand gesture recognition,” Pattern Recognition, vol. 55, pp. 137–147, 2016. View at: Publisher Site  Google Scholar
 H.R. Choi and T. Kim, “Modified dynamic time warping based on direction similarity for fast gesture recognition,” Mathematical Problems in Engineering, vol. 2018, Article ID 2404089, 9 pages, 2018. View at: Publisher Site  Google Scholar
 S. Adwan, I. Alsaleh, and R. Majed, “A new approach for image stitching technique using dynamic time warping (DTW) algorithm towards scoliosis xray diagnosis,” Measurement, vol. 84, pp. 32–46, 2016. View at: Publisher Site  Google Scholar
 Y. Tayal, R. Lamba, and S. Padhee, “Automatic face detection using color based segmentation,” International Journal of Scientific and Research Publications, vol. 2, no. 6, pp. 1–7, 2012. View at: Google Scholar
 P. Kakumanu, S. Makrogiannis, and N. Bourbakis, “A survey of skincolor modeling and detection methods,” Pattern Recognition, vol. 40, no. 3, pp. 1106–1122, 2007. View at: Publisher Site  Google Scholar
 H. Lahamy and D. D. Lichti, “Towards realtime and rotationinvariant american sign language alphabet recognition using a range camera,” Sensors, vol. 12, no. 11, pp. 14 416–514 441, 2012. View at: Publisher Site  Google Scholar
 N. Ohtsu, “A threshold selection method from graylevel histograms,” IEEE Transactions on Systems Man & Cybernetics, vol. 9, no. 1, pp. 62–66, 2007. View at: Publisher Site  Google Scholar
 K. He, J. Sun, and X. Tang, “Guided image filtering,” in Proceedings of the in European Conference on Computer Vision, pp. 1–14, Heraklion, Crete, Greece, September 2010. View at: Google Scholar
 F. Chang, C.J. Chen, and C.J. Lu, “A lineartime componentlabeling algorithm using contour tracing technique,” Computer Vision and Image Understanding, vol. 93, no. 2, pp. 206–220, 2004. View at: Publisher Site  Google Scholar
 H. S. Shin, C. Lee, and M. Lee, “Adaptive threshold method for the peak detection of photoplethysmographic waveform,” Computers in Biology and Medicine, vol. 39, no. 12, pp. 1145–1152, 2009. View at: Publisher Site  Google Scholar
 R. Zhou, J. Yuan, J. Meng, and Z. Zhang, “Robust partbased hand gesture recognition using kinect sensor,” IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1110–1120, 2013. View at: Publisher Site  Google Scholar
 C. Harris, “Geometry from visual motion,” in Active Vision, pp. 263–284, MIT Press, Cambridge, MA, USA, 1993. View at: Google Scholar
 S. Salvador and P. Chan, “Toward accurate dynamic time warping in linear time and space,” Intelligent Data Analysis, vol. 11, no. 5, pp. 561–580, 2007. View at: Publisher Site  Google Scholar
 M. Murugappan, N. Ramachandran, and Y. Sazali, “Classification of human emotion from EEG using discrete wavelet transform,” Journal of Biomedical Science and Engineering, vol. 3, no. 4, pp. 390–396, 2010. View at: Publisher Site  Google Scholar
 N. H. Dardas and N. D. Georganas, “Realtime hand gesture detection and recognition using bagoffeatures and support vector machine techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 11, pp. 3592–3607, 2011. View at: Publisher Site  Google Scholar
 S. Balakrishnama and A. Ganapathiraju, “Linear discriminant analysisła brief tutorial,” in Proceedings of the International Joint Conference on Neural Network, pp. 387–391, IEEE, Anchorage, AK, USA, 1998. View at: Google Scholar
 K. Fukunaga, Introduction to Statistical Pattern Recognition, Elsevier, Amsterdam, Netherlands, 2013.
 Z. Jin, J.Y. Yang, Z.S. Hu, and Z. Lou, “Face recognition based on the uncorrelated discriminant transformation,” Pattern Recognition, vol. 34, no. 7, pp. 1405–1416, 2001. View at: Publisher Site  Google Scholar
 J. Cheng, F. Wei, C. Li, Y. Liu, A. Liu, and X. Chen, “Positionindependent gesture recognition using semg signals via canonical correlation analysis,” Computers in Biology and Medicine, vol. 103, pp. 44–54, 2018. View at: Publisher Site  Google Scholar
 R. N. Khushaba, “Correlation analysis of electromyogram signals for multiuser myoelectric interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 4, pp. 745–755, 2014. View at: Publisher Site  Google Scholar
 S. Liu, Y. Liu, J. Yu, and Z. Wang, “Hierarchical static hand gesture recognition by combining finger detection and hog features,” Journal of Image and Graphics, vol. 20, no. 6, pp. 781–788, 2015. View at: Google Scholar
 X. Chen, C. Shi, and B. Liu, “Static hand gesture recognition based on finger rootcenterangle and length weighted mahalanobis distance,” in Proceedings of the SPIE 9897, RealTime Image and Video Processing 2016, SPIE, Brussels, Belgium, April 2016. View at: Google Scholar
 Z. Ren, J. Yuan, and Z. Zhang, “Robust hand gesture recognition based on fingerearth mover’s distance with a commodity depth camera,” in Proceedings of the 19th ACM International Conference on Multimedia—MM ’11, pp. 1093–1096, ACM, New York, NY, USA, November 2011. View at: Publisher Site  Google Scholar
 X. Xu, F. Lin, A. Wang, Y. Hu, M. C. Huang, and W. Xu, “Bodyearth mover’s distance: a matchingbased approach for sleep posture recognition,” IEEE Transactions on Biomedical Circuits & Systems, vol. 10, no. 5, pp. 1023–1035, 2016. View at: Publisher Site  Google Scholar
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