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Sensors and Materials, Vol. 31, No. 3 (2019) 923–937
MYU Tokyo
S & M 1825

High-performance Gesture Recognition System
Yun Wu,1 Shaoyong Yu,2 and Mei Yang2*
1

School of Software Engineering, Xiamen University of Technology,
No. 600, Ligong Road, Jimei District, Xiamen City 361024, China
2
School of Mathematics and Information Engineering, Longyan University,
No. 1 Dongxiaobei Road, Xinluo District, Longyan City, Fujian Province 364012, China(Received October 15, 2018; accepted January 30, 2019)

Keywords: gesture recognition, down sampling method, 2 × 2 average filter method, discrete wavelet
transform, symmetric discrete wavelet transform

With advances in technology, motion sensing has been applied in a wide range of devices.
The system used for motion sensing captures images via a camera and analyzes the acquired
images, also known as human–computer interaction, wherein gesture-controlled machines are
considered as the most convenient kind and are most commonly used. However, the existinggesture recognition algorithm often has a long computation time and uses a huge amount ofmemory, preventing the algorithm from being implemented in embedded systems. To alleviatethese problems, in this paper, we propose to reduce the computation time of the system by
employing the lifting-based discrete wavelet transform (DWT). The different frequency bands
featured in a lifting-based discrete wavelet can swiftly distinguish the face region from the handregion, reduce the image resolution, and thus reduce memory usage. In addition to recognizing
gestures in a swift and accurate manner, the gesture recognition approach proposed in this study
is also compatible with embedded systems. Our experimental results suggest that the proposed
approach can reduce the execution time by up to 66% while achieving a high identification rate.

1.

Introduction

In the rapid development of science and technology, human–computer interaction is playing
an essential role. Game consoles such as PS3 and Wii can all be directly operated via faces
and gesture recognition, thus enhancing the enjoyment of games. Skin color detection is often
applied in gesture recognition systems to roughly determine the position of humans, because
skin color is regarded as one of the most distinguishing features of humans. Although the skincolor of different races varies considerably, the difference mainly lies in the brightness rather
than in the hue,(1) and skin color is still a unique feature that can best distinguish humans fromother objects. For skin color detection, many approaches are available; for example, the RGB
color space can be converted into the YCbCr color space to take advantage of the clustering
effect of skin color particles in the YCbCr space, thereby capturing objects that fall under the
category of skin color for further analysis. Alternatively, the skin color can be filtered out*

Corresponding author: e-mail: wulv07@163.com
https://doi.org/10.18494/SAM.2019.2164
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 31, No. 3 (2019)by converting the RGB color space to the hue, saturation, value (HSV) color space and using
the features of this space.(2) Inevitably, in a real-life environment, there are many skin-colorlike objects, which impose certain difficulties in detecting skin color accurately. To this end,
background subtraction is often utilized to detect foreground patches or depth images, so as
to distinguish the foreground from the background,(3) which can lead to the accurate detectionof skin color blocks. Other relatively viable and mature gesture recognition methods include
extracting gestures by particle diffusion grouping,(4) the use of a hidden Markov model, handshape description using high-dimensional feature vectors,(5) as well as hand recognition usinga Haar feature coupled with a HOG feature.(6) To distinguish the two regions and analyze handblocks in particular, it is necessary to find the differences between hands and faces. Haarlike features are widely used to filter out facial features.(7) By cutting images into rectangular
blocks of various sizes and directions, Haar-like features can be used to calculate the sum of
block brightnesses by applying image integration while carrying out a comparison between
interblock brightnesses difference and the precalculated threshold value. However, the abovementioned Haar feature detection is a weak classifier, making it necessary to establish a feature
database of the target of interest in advance. With due practice, we can capture a good numberof stronger features, which can be applied to form a cascaded classifier, which will significantly
increase the detection accuracy. However, it may not be able to provide real-time operation
owing to the large number of features that must be compared.
Owing to the advances in technology, the pixels of images have been increasingly refined.
For skin color detection, CodeBook background modeling, Haar feature detection, and so forth,the computation time increases geometrically with the image size. To overcome this problem,in this paper, we propose the use of the discrete wavelet transform (DWT), a method capableof retaining a large amount of energy, to reduce the image resolution. Also, the characteristicfeatures obtained from the DWT can be used to quickly distinguish hand blocks from face
blocks in an image. That is, we can reduce the amount of follow-up system operation by
separating hand and face blocks. In addition, we propose a recognition method based on
gesture appearance, which has a high calculation speed and a high recognition rate of over 90%.
In this paper, we expound on the proposed method of using the DWT to rapidly distinguishhand and face regions. We also describe the process of hand identification and the system flow
in detail.

2.

DWT for Gesture Recognition

The approach used for extracting gestures is of essential importance in gesture recognition.
However, in addition to gestures, the system must also deal with the user’s face. In this regard,
the characteristics of the DWT are applied to distinguish the face from the hands to reduce thefollow-up processing time while analyzing the hand regions and extracting gestures. Figure
1 shows the flow chart of our system. First, DWT encoding is carried out on the input image,thereby reducing the resolution; the subsequent processing involving skin color detection andbackground modeling is conducted with the reduced image, decreasing the operation timeof the system. Then, the face and hand textures are analyzed using the high-low frequency

Sensors and Materials, Vol. 31, No. 3 (2019)

Fig. 1.

925

Flow chart of system.

information produced from the DWT, thereby distinguishing hand and face regions by thedifference between their textures.
2.1

DWT encoding

To speed up image processing, image reduction is commonly used to reduce the follow-up
processing time. Three methods are commonly used for this purpose, i.e., the down sampling
method, the 22 average filter method, and the DWT. All of them can be used to reduce theresolution and computational burden, where the DWT is most widely chosen for its excellent
energy concentration ability and multiresolution, which can separately process different
elements and largely retain original images upon reducing the resolution. However, theconversion process can be time-consuming.
In this study, we applied an upgraded version of the DWT, i.e., the lifting-based twodimensional DWT,(8) also known as the symmetric DWT (SMDWT), which can produce
the same image quality and values as the 5/3 lifting-based distortion-free discrete wavelet
converter. In the case of two-dimensional multi order operation, SMDWT appears to beconvenient. In addition, this new method features a short critical path, fast calculation, regularsignal processing, independent sub-band image processing, and so forth. Figure 2(a) shows thecoefficients of a symmetric low-frequency shielding matrix, Fig. 2(b) shows the coefficients of asymmetric low-high-frequency mask matrix, and Fig. 2(c) shows the coefficients of a symmetrichigh-low-frequency shielding matrix used in SMDWT. Degraded images obtained after
processing by SMDWT are shown in Fig. 3, where Fig. 3(a) shows low-frequency information,
Fig. 3(b) shows low-high-frequency information, i.e., the vertical texture, and Fig. 3(c) shows
high-low-frequency information, i.e., the horizontal texture.
Skin color is a very distinct feature for humans and is useful for distinguishing between
human bodies and other objects. Many methods can be used to detect skin color. According to
the literature,(1) the essential difference between the skin colors of oriental and Caucasian peoplelies in the brightness. On this basis, when brightness is removed from an image, particle swarm
optimization (PSO) for the skin color of various humans has a high clustering tendency, which

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Sensors and Materials, Vol. 31, No. 3 (2019)

(a)

(b)

(c)

Fig. 2. (a) Coefficients of symmetric low-frequency shielding matrix, (b) coefficients of symmetric low-highfrequency mask matrix, and (c) coefficients of symmetric high-low-frequency shielding matrix.

(a)Fig. 3.

(b)(c)(a) Low-frequency image, (b) low-high-frequency image, and (c) high-low-frequency image.

can achieve a rather high recognition rate. Thus, in the proposed method, the HVS color space
is utilized to detect skin color blocks.(2) The HSV color space divides the original RGB color
space into three dimensions: hue (H), saturation (S), and value (V). By converting the original
RGB color space to the HSV color space, the element with the greatest effect, brightness (V),will be filtered out, and the skin color is identified from the corresponding values of H and S
given as 1, if 0 ≤ H ≤ 80
Skin( x, y ) = .
0, if 0.1 ≤ S ≤ 0.75

(1)Figure 4(b) shows the skin color blocks of the image in Fig. 4(a) detected via Eq. (1), fromwhich it can be perceived that, although the representative feature of the human body is theskin color, there are many skin-color-like objects in a real-life environment; such noise mayundermine our experimental results. Therefore, the detected skin color blocks are fine-tuned as
described in the following sections.
2.2

Morphology filtering processing—erosion and dilation

A morphological operation refers to the integration of an image and structural elements
in a set operation to produce a new filtered image. With the dilation and contraction in theSensors and Materials, Vol. 31, No. 3 (2019)(a)Fig. 4.

927(b)(Color online) (a) Input image and (b) skin color block detected using Eq. (1).

morphology method, noise can be filtered out and unmatched objects can be connected. The
basic structural elements used in the morphological operation are shown in Figs. 5(a) and 5(b).
The noise is filtered out by using the eight neighbors of a pixel in this method.
2.3

Codebook backgroundTo capture the foreground, a background model is needed for reference. Codebook
background modeling is adopted to set up the background model in the proposed method. In
contrast to the previous background construction methods, the Codebook algorithm handles the
background models in complex scenes by quantization clustering, and every pixel is quantized
and represented by Codebook on the basis of the independent pixels in an image sequence. In
this algorithm, the color and brightness of each pixel are sampled and then compared withthe color distance and brightness in the color model, thereby determining whether the pixel
is part of the background. If the answer is positive, the pixel is quantized into groups of
Codeword. The background features are stored in the units of pixels; the Codebook of all pixels
pieces together a complete background. Unlike the Gaussian mixture model, which requires
the probability distribution to be calculated, Codebook only requires the color distance andbrightness range of a pixel. Therefore, Codebook has low complexity and low memory usage.
Also, in the initialization process, Codebook can swiftly extract the background of an image
with moving foreground objects. As an adaptive background update and compression method,
it is capable of handling local or global illumination changes, which is essential for real-time
image processing.
2.4

ForegroundIn this step, to filter out a background block with a color similar to human skin, after theforeground part is separated by the Codebook algorithm (see Sect. 2.4 of the 2.4 measure), theAND operation is performed on the foreground color and the skin color block is extracted by the 2.3
measure. The foreground skin color block is obtained, and the result is connected to the domain
to reduce noise interference (as shown in Fig. 6).

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Sensors and Materials, Vol. 31, No. 3 (2019)

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 5. (a) Eight neighbors of a pixel, (b) four neighbors of a pixel, (c) binarized image, (d) image eroded by eightneighbors, (e) image delayed by eight neighbors, and (f) dilated and contracted image.

(a)
Fig. 6.

2.5

(b)

(c)

(a) HSV skin color detection, (b) Codebook foreground, and (c) result of combining (a) and (b).

Features of DWTThe experimental results suggest that, when the edge texture is not taken into account, thetexture features of a human face and palm are considerably different. Since the horizontal
texture of a human face contains rich information, we calculate the horizontal texture strengthof connected blocks as described in this section. The low- and high-low-frequency information
can be obtained via the masking process of Figs. 2(b) and 2(c) in Sect. 2.1. From pastexperience, we found that the low- and high-low-frequency information represent the verticaland horizontal textures, respectively. In the case of facial textures, the horizontal texture
contains a large amount of information, while the vertical texture contains a small amountof information. In this regard, the following processing focuses on analyzing the horizontal
texture. To remove the interference caused by weak texture information, we first apply Eq. (3)to check the high-and-low-frequency information transformed by the wavelet transform. If theSensors and Materials, Vol. 31, No. 3 (2019)929

value of HL(x, y) is less than the threshold Th0, then the horizontal energy intensity is relatively
low and can be ignored; if not, HL(x, y) is expressed by HL(x, y) = 255. On a grayscale image,
we further confirm the approach where a horizontal Sobel filter is used to mask G [Eq. (2)] by
HL(x, y) = 255, as shown in Eq. (4). If the value convolved via the masked G is greater than theset threshold Th1, then S(x, y) = 1 is considered to be a texture point. Because the horizontal
texture is in a continuous form rather than individual points, if any point satisfying S(x, y) = 1
meets the above conditions, then Label(x, y) = 1 is marked as a point with a strong horizontal
texture, as shown in Eq. (5). This approach can phase-remove many independent points,
resulting in more accurate horizontal texture information.
1
2
1



0
0
G= 0


−1 −2 −1



(2)

255, if HL( x, y ) ≥ Th0
HL( x, y ) = 0, if HL( x, y ) < Th0 Th0 = 10(3)

1, if G ∗ g ( x, y ) ≥ Th1 & HL( x, y ) = 255
S ( x, y ) = 0, if G ∗ g ( x, y ) < Th1 & HL( x, y ) = 255where Th1 = 100(4)1, S ( x, y + 1) = 1| S ( x, y −1) = 1
Label ( x, y ) = 0, else(5)g(x, y) is a grayscale image, HL(x, y) is the pixel value of the original image processed by thesymmetric high-low-frequency mask matrix, S(x, y) is the marker of a texture point, and Label(x, y)is the marker of a strong texture point.
2.6

Segmentation of face and hands

The previous section suggested that a palm, which is rather smooth, contains less texture
information than a human face. According to experimental results, it can be perceived thatthe texture characteristics of a human face and hand vary considerably. On this basis, we havebeen able to distinguish human hands and faces by building up statistics regarding the numberof strong texture points in each connected block and examining the difference in the numberof texture points, thus increasing the recognition rate. On the basis of the sizes of various
blocks of facial skin color and their corresponding strengths for the consistency of texture
points, we established a look-up table as a threshold reference. Note that light will alter the
texture information to a certain extent. Therefore, our discussion only applies to experimental
settings with a stable light source. When the number of texture points exceeds a set threshold,930

Sensors and Materials, Vol. 31, No. 3 (2019)

the texture is regarded as a facial texture, as shown in the green frame in Fig. 7(b); if not, it isregarded as a hand texture, as shown in the red frame in Fig. 7(b).
The threshold table set up in this experiment is shown in Fig. 7(a). It can be perceived thatthe shorter the distance to the camera, the larger the number of strong texture points in theface. In addition, the high- and low-frequency information produced by the DWT may be lost
owing to the movement of the object. Therefore, it is challenging to recognize face blocks inthe case of fast movement. To overcome this problem, given that the movement of an object
is continuous and uninterrupted, when a block has been determined to be in a facial region,
the system carries out coordinate positioning and tracking, in which the block is given a fixed
coordinate. Subsequently, any block within a certain range of the set coordinate is automaticallyregarded as a face block.
2.7

Gesture extraction

In Sect. 2.6, the face and hand were distinguished by the difference in the number of strong
texture points. Therefore, instead of processing the entire image, only the blocks determined
as hand blocks will be read as the region of interest and then processed, to remove a number ofsystem operations in Fig. 8.

(a)

(b)

Fig. 7. (a) Graph of number of strong texture points and their distance to camera and (b) segmentation result ofhand and face.

Fig. 8.

(Color online) Region of interest.

Sensors and Materials, Vol. 31, No. 3 (2019)

3.

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Gesture Recognition

In terms of gesture recognition, the input image may be skewed, which is not conducive tothe system’s recognition process. Therefore, we first rotate the gestures, so as to closely analyze
their features. We identify the wrist and the joints of the fingers and palm, removing the parts
other than the palm. The feature data obtained by this analysis is utilized to distinguish thefingers and identify the gesture. The gesture recognition flow is shown in Fig. 9, where the four
processes are described in the following subsections.
3.1

RotateUnder normal circumstances, users’ gestures are very likely to be skewed. It is necessary torotate the gestures in this step to facilitate the subsequent processing. Prior to rotating the arm,
we need to find out the skew angle of the arm. For this purpose, least-squares estimation(9) is
adopted here, in which a regression line is obtained by minimizing the sum of the squares of all
the distances of the discrete points from the regression line. The rotation angle and slope can beobtained from the regression line. Next, the rotated image can be produced by introducing therotation angle into Eq. (6), as shown in Fig. 10.
 X   cos θ = Y  − sin θ

sin θ   x  
cos θ   y 

Fig. 9

Gesture recognition flowchart.

Fig. 10(Color online) Rotated Image.

(6)

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Sensors and Materials, Vol. 31, No. 3 (2019)

Hand feature analysis

Gestures are mostly made by placing the palm and fingers together, so the skin color blocks
below the wrist are disposable. To scale down the complexity of the operation, we removed
the part below the wrist, while the palm remained unchanged.(10) Figures 11(a) and 11(b) show
schematic diagrams of a human palm. It can be seen from Fig. 11(a) that the palm region isalmost square, making it easier for us to distinguish the palm from the hand. We next consider
the fingers. It can be seen from Fig. 11(a) that the lengths of the fingers and that of the palm
are similar, with the ratio approximately ranging between 1 and 1.4. Therefore, to remove thepart below the palm, we must first figure out the finger length in Fig. 11(b). For this purpose,
we carry out a scan down from a finger as shown in Fig. 11(c). Since the width in the lateral
direction increases markedly upon reaching the palm, we apply this feature to determine theposition of the base of the fingers and the finger length. The base of the fingers is shown by thesecond horizontal line in Fig. 11(d). We observed that the ratio of the finger length to the wrist
length is about 1–1.4. Therefore, with the base of the fingers as a reference, we determined thestart of the wrist as a distance of 1.2 times the finger length below the base of the fingers, as
shown by the third line in Fig. 11(d).
3.3Draw circle

We improved the method in Ref. 10 as reposted in this section. Considering that the method
proposed in this paper is affected by changes in radius, we derived a method of stabilizing

(a)

(b)

(c)

(d)

Fig. 11 (Color online) (a) Hand features, (b) hand features, (c) diagram for finding ends of fingers, and (d) handsegmentation diagram.

Sensors and Materials, Vol. 31, No. 3 (2019)933

the radius. As reported in the previous section, we have already obtained the hand blocks to
be removed, the width of the wrist, the center wrist, and so forth. As shown in the schematic
diagram in Fig. 12(b), we found that we can distinguish the palm by drawing a circle with the
middle point of the wrist as the center and the palm length as the radius. Next, by increasing
the radius to 1.35 times the palm length, the fingers can be separated as shown by the blue line
in Fig. 12(c); however, there may be certain errors when it comes to the thumb. To reduce theerror, we draw semicircles with the points “1/4 wrist width” from the right and left of the palm
center as an attempt to more accurately identify the thumb, as shown by the red line in Fig.
12(c).
3.4

GestureWe first use the number of fingers to distinguish the gestures. A gesture with one finger iscertainly gesture 1; a gesture with two fingers may be gesture 2, 6, or 7; a gesture with threefingers may indicate gesture 3 or 8; a gesture with four fingers may be gesture 4 or 9; anda gesture with five fingers must be gesture 5. Thus, only gestures with two, three, and four
fingers require further identification. In Fig. 12(b), the orange blocks represent the extracted
fingers. The coordinates of the highest point of each orange block are recorded; then the highest
and lowest points among these coordinates are found. In each diagram in Fig. 13, the yellow
line is no higher than the apex of the semicircle, and the blue line passing through the center
point of the wrist divides the diagram into left and right sides. Figure 13(a) shows a schematic
diagram of gesture 2. It can be seen that the highest and lowest points are both above the yellow
line. Figure 13(b) is gesture 6, whose lowest point is below the yellow line, and its highest andlowest points are to the left and right of the center of the palm, respectively, that is, they lie on
opposite sides of the blue line [Fig. 13(c)], i.e., gesture 7, has its lowest point below the yellow
line, and the highest and lowest points are on the same side of the blue line. A gesture withthree fingers may be gesture 3 or 8. The highest and lowest points of gesture 3 are above theyellow line, as shown in Fig. 13(d), whereas for gesture 8, as shown in Fig. 13(e), the highest andlowest points are on opposite sides of the yellow line. Finally, a gesture with four fingers may
be gesture 4 or 9. As shown in Fig. 13(f), the highest and lowest points of gesture 4 are above
the yellow line, whereas the highest and lowest points of gesture 9 are on opposite sides of the

(a)

(b)

(c)

Fig. 12. (Color online) Schematic diagrams of (a) the center of the wrist, (b) the palm with a circle, and (c) the palm
with semicircles.

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Sensors and Materials, Vol. 31, No. 3 (2019)

(a)

(b)

(e)

(c)

(f)

(d)

(g)

Fig. 13. (Color online) Diagrams of the relative positions of gestures.

yellow line [Fig. 13(g)]. By connecting the various domain markers, the coordinates of the
top ends of the fingers can be obtained; such coordinates which are used to compare between
the highest and lowest points and the information acquired from the previous step, including
the wrist center and radii. Taking into account the relative positions and relevant information,
we can recognize gestures, regardless of the distance, so as to avoid possible errors caused bydifferent distances to the lens.

4.

Experimental Results

In this work, the computer used for the simulation had an Intel Core i5-2410 CPU with 2.3
GHz working frequency and 4 Gb RAM. We implemented our method using Microsoft Visual
Studio 2008 on a Windows platform.
A Logitech web camera with an image resolution of 640 × 480 was used to input the test
images. The face and hand recognition rates were obtained after testing 500 images; weemployed 9000 test images to find the accuracy rate for gestures 1–9, which are shown in
Fig. 14, and the experimental results can be found in Tables 1 and 2. Table 1 shows that facedetection has a relatively high accuracy rate, where the identification failures that occasionallyoccurred were mostly caused by the sides of faces with a very large turning angle, while faces
within a certain range of angles can be easily detected. From Tables 2 and 3, it can be perceived
that the gesture recognition system has a high speed and a high recognition rate of over 90% forgestures 1–9. Examples of unsuccessful samples are shown in Figs. 15–17, where several factors
are considered to have caused identification failures, such as the nonpositive gesture shown in
Fig. 15, the fingers too widely apart or too close, leading to errors when detecting finger tips, as
shown in Fig. 16, and a disfigured hand shape due to skin color detection, as shown in Fig. 17.
Table 3 gives the execution speeds of the various sections of the system; the average speed of
the overall system is roughly 30 fps, which can satisfy real-time requirements.

Sensors and Materials, Vol. 31, No. 3 (2019)935

Fig. 14. (Color online) Examples of successful samples.
Table 1
Hand recognition results.
Gesture Total no. of samples Successful samples Unsuccessful samples Success rate (%)
1
1000
983
17
98.3
2
1000
991
9
99.1
3
1000
992
8
99.2
4
1000
971
29
97.1
5
1000
973
27
97.3
6
1000
961
39
96.1
7
1000
947
53
94.7
8
1000
980
20
98.0
9
1000
981
19
98.1
Table 2
Face recognition results.
Total no. of samples Successful samples Unsuccessful samples
500
481
19

Success rate (%)
96.2

Table 3
System execution times.
WT for face recognition
Execution time (ms)
DWT transform
14.11209
Skin detect and morphology
3.51457
Foreground3.72797
Texture and face detect
2.44290
Total
23.79753fps
70.86
284.53268.24409.35
42.02

Gesture recognition system execution times
Execution time (ms)fpsRotate1.60219624.15
Hand feature analysis
0.386152589.67
Draw circle
0.923501082.84
Gesture0.489202044.15Total
3.40104
294.03
Total system
Execution time (ms)fps29.61921
33.76

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Sensors and Materials, Vol. 31, No. 3 (2019)(a)Fig. 15.

(b)(c)(Color online) (a) Tested gesture 5, (b) finger segmentation diagram, and (c) result diagram.

(a)

(b)

(c)

Fig. 16. (Color online) (a) Tested gesture 3, (b) finger segmentation diagram, and (c) result diagram.

(a)Fig. 17.

5.

(b)

(c)

(Color online) (a) Tested gesture 9, (b) finger segmentation diagram, and (c) result diagram.

Conclusion

We proposed the use of the symmetric DWT for gesture recognition to distinguish handblocks from face blocks in an efficient and stable manner. The experimental results verified
that the proposed method can significantly reduce the amount of computation involved in thesystem. In addition, by taking advantage of the features of the DWT, the hands and face canbe distinguished much faster than by Haar feature detection, where the latter requires feature
matching. Also, the face and hand blocks can be identified rather accurately with the DWT. For
hand recognition, we proposed a simple and stable method to divide the palm from the fingers,
whose recognition rate was over 90% in experiments. Because no complex sensing components
are used in our method and the symmetric DWT used in our system is notably simpler thanthe original DWT, hardware-based development is promising, following further research anddevelopment.

Sensors and Materials, Vol. 31, No. 3 (2019)937

Acknowledgments
This work was supported by the National Science Foundation of Fujian Province of China (No.
2018J01575) and the Project of Scientific Climbing Plan by Xiamen University of Technology (No.
XPDKQ18015).

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About the Authors
Yun Wu received her Ph.D. from Xiamen University in 2007. Her researchinterests lie in the areas of artificial intelligence and big data. Her scientificcontribution to AI has more to do with soft computing and clustering
algorithms. (ywu@xmut.edu.cn)

Shaoyong Yu received his Ph.D. from Xiamen University in 2017. He is also
an associate professor in the School of Mathematics and Information
Engineering, Longyan University. His research interests lie in the areas ofcomputer vision and deep learning. (82018007@lyun.edu.cn)

Mei Yang is a lecturer and teacher in the School of Mathematics andInformation Engineering, Longyan University. Her research interests lie in
the areas of software development and artificial intelligence.
(wulv07@163.com)