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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Dr. Shahriar Mohammadi, KNTU University, IranProf. Beonsku An, Hongik University, korea
Dr. Guanbo Zheng, University of Houston, USA
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Prof. Ilkyeun Ra, University of Colorado Denver, USA
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Dr. Yulei Wu, Chinese Academy of Sciences, China
Mr. Anup Thapa, Chosun University, korea
Dr. Vo Nguyen Quoc Bao, Posts and Telecommunications Institute of Technology, Vietnam
Dr. Harish Kumar, Bhagwant institute of technology, India
Dr. Jin REN, North china university of technology, China
Dr. Joseph Kandath, Electronics & Commn Engg, India
Dr. Mohamed M. A. Moustafa, Arab Information Union (AIU), Egypt
Dr. Mostafa Zaman Chowdhury, Kookmin University, Korea
Prof. Francis C.M. Lau, Hong Kong Polytechnic University, Hong Kong
Prof. Ju Bin Song, Kyung Hee University, korea
Prof. KyungHi Chang, Inha University, Korea
Prof. Sherif Welsen Shaker, Kuang-Chi Institute of Advanced Technology, China
Prof. Seung-Hoon Hwang, Dongguk University, Korea
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Prof. Chongyang ZHANG, Shanghai Jiao Tong University, China
Dr. H K Lau, The Open University of Hong Kong, Honh Kong
Prof. Ying-Ren Chien, Department of Electrical Engineering, National Ilan University, Taiwan
Prof. Mai Yi-Ting, Hsiuping University of Science and Technology, Taiwan
Dr. Sang-Hwan Ryu, Korea Railroad Research Institute, Korea
Dr. Yung-Chien Shih, MediaTek Inc., Taiwan
Dr. Kuan Hoong Poo, Multimedia University, Malaysia
Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
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Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Volume. 6 Issue. 5

6J

1 Ontology Modification Using Ontological-Semantic Rules

1046

Anna YANG*, Sung Moon CHUN**, and Jae-Gon KIM*
*School of Electronics and Information Engineering, Korea Aerospace University, Korea**Insignal, Korea

Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

1046

Detection and Recognition of Hand Gesture for
Wearable Applications in IoMT
Anna YANG*, Sung Moon CHUN**, and Jae-Gon KIM*
*School of Electronics and Information Engineering, Korea Aerospace University, Korea**Insignal, Korea
nayang@kau.kr, smchum@insignal.co.kr, jgkim@kau.ac.kr

Abstract— To support an efficient media consumption in
wearable and IoT (Internet of Things) environments, thestandardization of IoMT (Internet of Media-Things) is in the
progress in MPEG (Moving Picture Experts Group). In thispaper, we present a method to detect and to recognize hand
gestures for generating hand gesture-based commands tocontrol the media consumption in smart glasses. First, we
present a detection method that utilizes depth image obtainedby incoming stereo image sequences and skin color information
in a combined way. Secondly, we are going to present the
representation of detected hand contours based on Bézier curve
as metadata to provide an interoperable interface between adetection module and a recognition module in an IoMT
framework. In addition, the comparison with existing standard
tools that can be used for hand gesture representation is given.
In the recognition module, the detected hand contour isreconstructed by parsing delivered metadata. A set of hand
gestures featured with diverse combination of open fingers androtational angles is used for the hand gesture recognition in the
proposed recognition method. Finally, the recognized hand
gesture is mapped into one of the pre-defined set of gesturecommands. Experiment results show that the proposed method
gives quite stable performance of detection and recognition ofhand gesture along with interoperable interface between bothprocessing modules.

Keyword— MPEG Internet of Media-Things (IoMT), Smart
Glasses, Hand Gesture Recognition, Hand Gesture Detection,
Bézier curve

R

I. INTRODUCTIONECENTLY, the standardization of Internet ofMedia-Things (IoMT) is undergoing in MPEG to
support efficient media consumption in wearable and IoT(Internet of Things) environments [1]. The normative parts of
the IoMT are mainly the specification of API (Application
Programming Interface) and the representation of———————————————————————
Manuscript received Aug. 23, 2017. This work was supported by Institutefor Information & communications Technology Promotion (IITP) grant
funded by Korean Government (MSIT) (R0127-15-1015).
Anna Yang and is with School of Electronics and Information
Engineering,
Korea
Aerospace
University,
Goyang,Korea(nayang@kau.kr).
Sung Moon Chun is with Insignal, Seoul, Korea (e-mail:smchun@insignal.co.kr).
Jae-Gon is with School of Electronics and Information Engineering,
Korea Aerospace University, Goyang, Korea (corresponding author toprovide phone: +82-2-300-0414; e-mail: jgkim@kau.ac.kr).

information to be delivered over the interfaces between
Media Things (called as MThings) that can be wearable/IoT
devices or processing units in media-centric IoT andwearable applications. On the other hand, as hand gestures
are attracting more attention as NUI (Natural User Interface)of wearable devices such as smart glasses, efficient detection
and recognition of hand gestures is strongly required [2]. In
this paper, we propose a hand gesture detection andrecognition method for controlling the media consumption
and/or wearable device itself. In addition, the XML(eXtensible Markup Language)-based description of hand
gesture is presented to support interoperable interfaces in thecontext of IoMT standard. In the use cases of gesture-based
wearable applications in IoMT [1], it is likely required thatgeneral hand gestures having any contour and/or trajectoryshould be described in an interoperable way in terms of two
aspects: 1) hand gestures to be mapped into commands thatare used for interactions between a user and a wearable
device should be general enough to support diverse potential
use cases; 2) detection of hand gestures and their recognition
can be done separately in different processing units (PUs) tocope with the limitation of computational power of thewearable device.
We adopt a simple method of gesture detection thatcombines depth map and color image acquired from theincoming stereo image sequence, and represents the detected
hand contour based on a Bézier curve as XML-based
metadata. The detected hand contour delivered as a form of
metadata is reconstructed in a recognition module. The
reconstructed hand contour is recognized as one of the set ofhand gestures predefined in a given application. Diverse
types of hand gestures with different open fingers and handangles can be identified mainly based on convexity defects ofhand contour. A post processing is applied on the recognition
results of consecutive frames in temporal domain to obtain
more reliable results. In addition, the results of comparison
with the existing MPEG standards that can be used fordescription of hand gesture are presented.
The rest of the paper is organized as follows. In Section II,
we present an introduction of MPEG IoMT andgesture-based wearable application scenarios considered in
IoMT. A gesture detection method is presented in Section III.
A method of hand contour description using Bézier curvealong with the comparison with the existing MPEG standard
is presented in Section IV. In Section V, a hand gesturerecognition method using the described hand contour ispresented, and experimental results are presented in Section
VI. Finally, the conclusions are given in Section VII.
Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

II. GESTURE-BASED WEARABLE APPLICATION IN IOMT
Gesture-based smart glasses applications are considered as
one of the typical wearable use cases in IoMT [1], [3]. Whena user wears smart glasses, hand gesture is becoming apromising user interface that allows a user to use both hands
without operating an input device such as keyboard or mouse,
etc. In such applications, in order for a user to control
efficiently media consumption, the hand gesture recognition
should be supported in an IoMT framework.
As shown in Fig. 1, user (user), MThing, and processing
unit (PU) are key functional components of an IoMT
framework. Media Thing is specified as a Thing capable ofsensing, acquiring, actuating, or processing of media or
metadata [1]. Smart glasses and processing unit are kinds ofMThing. In order to support various types of hand
gesture-based use cases including the control of media
consumption in a wearable device and/or device itself, hand
gestures which include static ones or dynamic ones such as a
trajectory of hand motion should be recognized into a user
invoked command.
As shown in Fig. 1, the incoming stereo image sequenceacquisitioned by a stereo camera mounted on the smart
glasses is transmitted to a PU, in which user’s gesture is
detected (① in Fig. 1). It is assumed that gesture detection
and gesture recognition are more likely to be performed inseparate PUs with the considerations as follows: 1) The
detected hand contour or trajectory can be recognized as oneof various types of gesture to be mapped into a command
depending on the given application rather than limiting the
gesture types, and/or 2) the computational power of a PU
may not be sufficient to perform both processings ofdetection and recognition in a wearable environment [4], [5].

1047

mapped into a user command. The gesture-based commands
are predefined in many different ways according to the given
application.
The generated command controls the wearable device
and/or the application software running on the wearable
device (③ in Fig. 1). In this way, the use case of efficient
media consumption in a wearable device is supported basedon hand gestures.
III. HAND GESTURE DETECTION
Fig. 2 illustrates the overall procedure of hand gesturedetection that combines depth map and color image extracted
from the incoming stereo image sequence. A depth map isgenerated by stereo matching of the incoming stereo images.
Due to the nature of the smart glasses, it is assumed that auser’s hand pose is given at a certain short distance range(30cm ~ 50cm) from the camera. Based on this assumption, arough hand region is simply obtained by thresholding thevalue of the depth map. Then, the detected rough hand region
is refined by using color information and morphologicalfiltering, which results in more accurate hand contour.
Start
Input the L,R
Stereoscopic Image
Stereo Matching &Generate Depth Map
Extracting Hand Region
Using Depth Map

MThing: Smart Glasses

Correlate with Color Image

Display
Applications

Boundary Filtering(Erosion, Dilation)

Pre-Processing
Stereo Camera

MThing: Processing Unit

MThing: Processing Unit

Hand Motion
Trajectory
Extraction

Fig. 2. Overall procedure of hand gesture detection

Gesture Recognition Module

Gesture Detection Module
Hand
Contour
Extraction

Detect the Hand

③

①

②

Metadata
Parsing

GestureRecognition

IV. HAND GESTURE DESCRIPTION
User

Metadata Generation

Command Mapping

Fig. 1. A framework of IoMT for supporting use cases of hand gesture based
wearable applications

Under this assumption, we need to specify a normativeinterface between both PUs performing detection andrecognition, respectively. In aspect of standard, we focus on
the representation of the detected hand contour to bedelivered to the recognition module (② in Fig. 1). In otherwords, a hand region is detected and its contour is describedas metadata in the hand detection module. Then, in the hand
gesture recognition module, the detected hand contour isreconstructed by parsing the metadata delivered from thedetection module. Finally, the reconstructed contour is

As mentioned before, the detected hand contour to bedelivered to a recognition module should be represented in aninteroperable way to support diverse types of hand gesturesin an IoMT framework. In this paper, we present an XML
schema for describing hand contour based on Bézier curve.
In addition, we briefly introduce the existing MPEG tools of
MPEG-U and MPEG-7 Curvature Scale-Space (CSS) thatcan be used for hand gesture description, and present thecomparison results between the MPEG-7 CSS and the Bézier
curve based description in terms of several aspects.
A. Bézier Curve Based Description
As shown in Fig. 3, a hand contour consists of a set ofconsecutive curves each of which is represented by using
nth-order Bézier curve given by Eg. (1). The nth-order Bézier
curve is expressed by (n + 1) points, which consist of astarting point, an end point, and a few control points that

Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

determine the curvature of the curve. In this paper, we use a
cubic Bézier curve with the order three, so there are twocontrol points P1, P2 in a curve representation as shown in Fig.
4.
Fig. 3 shows an example of the result of this representation
process. The given hand contour consists of a set of curves
each of which is expressed by four Bezier points in the caseof cubic Bézier curve. For example, four Bézier points areshown in Fig. 3 (b) (starting point, two control points, andend point are given as (P0x, P0y), (P1x, P1y), (P2x, P2y), and (P3x,P3y), respectively). As a result, a list of Bezier points
representing a set of consecutive curves corresponding to thedetected hand contour is obtained as shown in Fig. 3 (a).
n
n
n −i
Bezier ( n , t ) = f ( t ) =    ⋅ (1 − t ) ⋅ t i
i=0  i 

Find Contour

Number of points on the contour (nPts)
Set max_error to be allowed in curve fitting
Fitting Bezier curve
nPts <= 3 or 2
N

(1)

(P0x, P0y), (P1x, P1y), (P2x, P2y), (P3x, P3y)
(119, 20), (111, 23), (120, 58), (121, 65)(121, 65), (121, 65), (125, 81), (122, 83)(122, 83), (119, 85), (117, 75), (117, 73).
.
.
(131, 64), (130, 56), (127, 21), (119, 20)(a) Hand gesture representation
using a set of Bézier curves(b) Bezier point list of hand gestureFig. 3. An example of Bézier cuve representation

Fig. 4. The 3rd-order Bézier curve representation using four points (P0 : startpoint P1, P2: control points P3: end point)

Fig. 5 shows an algorithm for representing a hand contour
using a set of Bézier curves. To draw a given hand contour
using a set of Bézier curves, the whole contour is iteratively
divided until the fitting error between the Bézier curve based
represented curve and the given contour is less the allowed
error predefined. In other words, at the beginning, the entire
hand contour is represented by a single Bézier curve. Then, if
the fitting error using a Bézier curve is larger than a preset
threshold (max_error), then the given contour is divided into
two and each curve is represented by a Bézier curve. This
contour dividing and representation process is repeated until
the required accuracy is met. The fitting error can be definedas the distance between two curves. If there are less than four
points left on the contour, then joining the remaining points
are not bitted by a Bézier curve. As a result, the accuracy ofcontour representation is adjusted by setting the value ofmaximum fitting error, and the number of Bézier curve isdetermined accordingly.

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Devide curve

fitting_errorNfitting_error< max_error

Y

Draw Bezier curve

Fig. 5. A procedure of representation for Bézier Curve based hand gesture

To describe a list of Bézier points corresponding to thedetected hand contour as XML-formatted metadata, we
design a metadata schema as shown in Fig. 6. The detected
hand contour can be reconstructed by parsing the delivered
metadata in the recognition module. Fig. 7 shows an exampleof the reconstructed hand contour from the metadata.
The types of hand gesture can be static or dynamic. In thecase of dynamic type, a trajectory of hand motion can bemapped into a user-invoked command. As shown in Fig. 6,
the HandGestureType allows us to describe static gesture or
dynamic gesture by using the element of HandContour or
HandTrajectory, respectively. A hand contour consists of aset of consecutive curves each of which is represented by a
Bézier curve. The set of consecutive curves are described by
the element of GroupBezierCurve. A Bezier curve isrepresented by a start point, end point, and control points.
The end point of a curve is identical to the start point of thefollowing curve since a set of curves are successively
connected.

(a)Schema for HangGestureType and HandContourType(b)

Schema for GroupBezierCurveTpe and BezierCurveType

Fig. 6. Schema for hand gesture representation using Bézier curve

Therefore, as shown in Fig. 6 (b) of GroupBezierType, theInitialStartPoint describes the start point of the first curve,
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

and each of the consecutive curve is described by using the
CPoints (control points) and the element of StartEndPoint. In
this way, redundant description of the same point which is an
end point and a start point of successive curves is avoided.

Fig. 7. Reconstructed hand contour by parsing the delivered metadata

B. Comparison with the existing MPEG Tools
There are few tools that can be used to describe hand
gesture or hand contour in the MPEG such MPEG-7 CSS and
MPEG-U Part 2. In this sub-section, we present the review ofthe existing such tools, and present the comparison results
between MPEG-7 CSS and Bézier curve based description ofhand contour in the context of IoMT.
MPEG-U Part 2 specifies Advanced User Interaction (AUI)
interface to enhance interaction between scene descriptions
and system resources [6]. In the data format of the AUI
interface specified in MPEG-U Part 2 as shown in Table 1, a
set of hand postures and gesture patterns have been specified
to support the intuitive hand-based interaction for scene
description.
TABLE IDATA FORMAT OF AUI INTERFACE SPECIFIED BY MPEG-U PART 2
Pattern
Geometric
Symbolic
Hand Posture
Hand Gesture
Touch

Type
Point, Line, Rect, Arc, Circle
Victory, Heart, Rock, Scissors, Paper,
Okay
OpenPalm, Fist, Pointing, Thumb-Up,
Thumb-Down, Grap
Push, Pull, Slap, Slap_right, Slap_top,
Slap_bottom,
Circle_clockwise,
Circle_anti-clockwise, waving, check
Tap, Double Tab, Press, Dragg, Rotate,
Flick

Composition

MPEG-7 specifies a set of visual descriptors to describe
various visual features mainly focusing on content-based
indexing and retrieval applications [7]. In MPEG-7, theshape descriptor of the curvature scale space (CSS) is directly
related to the description of hand contour and hand motion
trajectory, respectively
The MPEG-7 CSS descriptor which is a contour based
shape descriptor treats shape boundary as a 1D signal, andanalyzes this 1D signal in a scale space [7], [8]. By examining
zero crossings of curvature at different scales, theconcavities/convexities of shape contour are found. These
concavities/convexities are useful for shape description
because they represent the perceptual features of shapecontour.

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The existing MPEG-U Part 2 and MPEG-7 CSS may not
be enough for describing hand gesture/contour in the context
of IoMT as follows. An environment of hand gesturerecognition in smart glasses is slightly different with that is
considered in MPEG-U in terms of camera view and camera
location. A smart glass is worn on a user’s face and handposture is acquisitioned within a short distance range while itis assumed that the viewpoint of the camera is located on theuser’s front torso in MPEG-U. It is likely that more rich andelaborate gestures are used in smart glasses to support
various hand gesture based use cases. However, a set ofpatterns of hand posture and hand gesture defined in
MPEG-U Part 2 are very limited.
The MPEG-7 CSS is a shape descriptor mainly focusing
on the applications of content-based indexing and retrieval,
therefore it has a few limitations in in the context of IoMT as
follows. The characteristic of rotation invariant
representation of the CSS would be a shortcoming since handpostures with different angle could have different meanings
in the description of hand contour. In addition, the detected
hand contour should be reconstructed from the description to
be recognized, which is not supported by CSS. As shown inthe comparison results of Table 2, the Bézier curve based
description meets all functionalities required in the hand
gesture based wearable applications in IoMT.
TABLE IICOMPARISON BETWEEN MPEG-7 CSS, MPEG-U PART 2, AND BÉZIER CURVEBASED DESCRIPTION TOOLS IN TERMS OF FUNCTIONALITIESKeyFunctionalities
Differentiation ofrotation
Applicability ofdiverse recognition
methods
Extensibility ofgesture types
Reconstructability

MPEG-7 CSS

MPEG-U Part 2

Beizer Curve

X

O

O

X

O

O

O

X

O

X

−

O

In terms of hand contour description, we present thedetails on the experiment of comparison between MPEG-7
CSS and Bézier curve based description in Table 3. The
results shows that both descriptors give similar description
performance in terms of complexity and efficiency(description size). However, only the Bézier curve based
descriptor allows the reconstruction of contour from thedescription as well as scalable description.
V. HAND GESTURE RECOGNITION
Fig. 8 shows the overall procedure of hand gesturerecognition. Delivered metadata describing the detected
contour is parsed into a list of Bezier points from which thedetected contour is reconstructed. In order to recognize thehand gesture from the reconstructed hand contour, as an
initial step, we generate a reference mask from the contour ofopen palm of a user. A reference mask gives initial values offeatures that are extracted and used as reference values for thesubsequent recognition step such as convexity defects,
degree of rotation, and gravity center.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017
TABLE IIIRESULTS ON THE COMPARISON BETWEEN MPEG-7 CSS AND BÉZIER CURVEBASED DESCRIPTION

Items

Bézier curve based
descriptor

MPEG-7
CSS
descriptor

Comment

Contour of theobjectContour ofApplicability
Moving trajectory
Objectof the object
Scalable in terms ofPossible to trade off
Scalability
description size and Not available contour accuracy
contour accuracy
and description size
Reconstruction
Available
Not available
Computational time
Computational
Average ~for generating
Average ~ 5msec
Performance
20msecdescriptor fromhand contour
Hand contour:
Average 95Byte
Average Both cases are very
Description Size
Hand trajectory:
15Byteslight weight
Average 36Byte
General contour
Shape
Main
description,
similarity
Applicationstransmission and matching andreconstruction
indexing

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spread by checking the defects inside of ellipse. Finally, thegesture of ‘OK’ sign can be recognized by checking theexistence of a closed contour inside of the hand contour.
As the result of the recognition, the commandcorresponding to the recognized hand gesture is generated by
mapping the recognized hand gesture into one of the set ofcommands predefined in a given application.
Reconstruct handcontour by parsing thedescription
Mask Generation
Get Open palm
from the first frame
Find Features(7 Defects, Degree ofRotation, Center of Gravity)
Generate theReference Mask
Gesture Recognition
Reference Mask
Overaly

Recognize the Hand
GestureUsing convex hall, we can obtain some defects whichallow us to figure out the position of each finger, and thegradient moment is used to obtain the center of gravity anddegree of rotation. The gradient of moment μ p ,q and theFig. 8. Overall procedure of hand gesture recognition from reconstructed handcontour by parsing the delivered metadata

degree of rotation θ are obtained from the palm region byusing Eg. (2) and Eg. (3), respectively. In order to obtainpalm region, the finger region is removed from the handregion by morphological operations.
andmean acoordinate of pixels, x and y mean a mean of and in
the palm region, respectively.
pq(2)
μ p ,q =(x − x) ( y − y)( x , y )⊂ R

1
2

θ = tan −1

2 μ1,1

μ2,0 − μ0,2(3)

In the recognition step, the reference mask issuperimposed on the reconstructed hand contour for eachframe depending on the rotation of degree. In this way, eachfinger’s state of open or close is identified by comparing theinitial defects and the current defects. Fig. 9 shows the details
on the recognition algorithm, in which states of each finger aswell as the degree of rotation are identified based on theinitial features of the reference mask.
The details on the algorithm for hand gesture recognition
are given in Fig. 9. When the reconstructed hand contour isinputted for recognition of hand gesture. First of all, 7 defects
which are used for recognition of open finger position arefounded by using convexity defect algorithm. To match
between the reference mask and reconstructed hand contour,
after then, the degree of rotation of reconstructed handcontour should be calculated by using the gradient of moment.
In this way, the reference mask is able to be mapped into thehand gesture contour according to its rotation.
In order to figure out whether the finger’s state is open or
close, an ellipse is drawn in the region finger, and if there are
more pixels in the finger region than the threshold value
inside of the ellipse, it is regarded that the corresponding
finger is spread. It can be also recognized which finger isFig. 9. Detains on the procedure of the hand gesture recognition

By the way, the rotation of reference mask may beincorrectly calculated by the center of gravity when few
fingers are opened or no finger is opened, which results indirectionally biased form as shown in Fig. 10 (b) and (c). To
recognize a rotated hand correctly by overcoming thisproblem, the rotational degree of reference mask should be
revised according to the rotation of given hand contour.
To address this issue, the rotational angle of the referencemask is obtained more accurately by using Eq. (4). In otherwords, we consider an additional factor of palm rotational
angle as well as both rotational angles of the incoming hand
gesture and the reference mask. We use a morphologicalmethod to obtain either palm region or finger regions only.
Then, the palm region is used for the calculation of rotation,and mask overlay with the consideration of the rotation.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 20171051

for the ‘OK’ sign, and the recognition result depending on the
degree of rotation of the hand is shown in Fig. 15 (c). In thisway, any hand posture can be identified and mapped into thecorresponding gesture based user’s command in wearable
applications
(a)
(b)
(c)
(a) A reference mask superimposed on the open hand(b) A reference mask superimposed on the biased hand(c) A reference mask superimposed the rock position handFig. 10. An example of reference mask rotated depending on hand poses

As shown in Eg. (4), the value of Rref _ mask , the angle ofreference mask that should be rotated when it issuperimposed on the hand contour is compensated with thedifference of rotational angle of palm and gesture.
(4)
R
= (A
−A
+ (A
−A
)) ⋅ πref _ mask

gesture

mask

palm

gesture180

A gesture : rotational angle of the hand gesture in the current frame(a) Hand gestures with different postures of hand

A mask : rotational angle of the reference mask
A palm : rotational angle of finger removed palm in the current frame

The obtained finger region is used for identifying whether
a finger is open or not. In other words, we apply an ellipse toeach finger regions, and determine the existence of finger
according to the number of pixel lies inside the region
overplayed ellipse. If the number of pixel is larger thanthreshold, then the existence of finger is true, which ismarked ‘1’.
Finally, a post processing is applied on the recognition
results of consecutive frames in temporal domain to reduce
recognition error in each frame. The recognition error is
occurred by not only in the process of changing hand gestures
but also by fine shaking of hand. By accumulating the resultof each frame, more reliable recognition result can beobtained for a given duration in the temporal post processing.

(c) Hand gesture with different
number of finger(b) OK sign

Fig. 11. A set of hand gestures to be recognized in the experiment

VI. EXPERIMENTAL RESULTS
In the experiment, a set of hand gestures to be recognizedis given in Fig. 11, which includes diverse types of hand
gesture with different combination of open fingers, different
hand postures with the same number of fingers, and ‘OK’
sign. Using the seven defect points, it is possible to recognize
which fingers are open as well as the number of open fingers.
The proposed method uses the number of finger contour tofigure out the number of open finger, and the position of open
finger is recognized by using ellipse and convexity defects.
Example results of recognition are as shown in Fig. 12. As
a result of hand gesture recognition, the position of eachfinger is obtained as ‘1’ (open) or ‘0’ closed. The binary code
representing the position of five fingers are compared withthose of the predefined gestures. Finally, the gesture image
corresponding to the matched gesture is displayed as a final
result as shown in Fig. 13. In addition, the gesture of ‘OK’sign can be recognized by checking the existence of a closed
contour inside the contour.
Some examples cases of final recognition results areshown in Fig. 13. The left and right images are the stereo
images acquired by the stereo camera, and the centered small
image shows the final recognition result. The example results
for the same number of fingers with different postures areshown in Fig. 13 (a). Fig. 13 (b) shows the recognition result(a) Recognition results of each finger’s position
FingerBin

FingerBin

Finger

Bin

THUMB

1

INDEX

1

THUMB

0

THUMB

0

INDEX

1

INDEX

1

MIDDLERING

1

MIDDLE0

MIDDLE

1

1

RING

0

RING

0

LITTLE1

LITTLE0

LITTLE0(b) Binary code related with (a) for representing each finger’s position
Fig. 12. Example results of hand gesture recognition

In the experiments, to measure the recognition accuracy ofthe proposed method, we use a pre-recorded video in addition
to real-time video capturing hand gesture. The experimental
conditions are summarized in Table 4, and the recognition
accuracy is measure by using Eq. (5).
Recog _ accuracy(%) =num _ of _ accurately _ recognized _ frame
num _ of _ total _ frame(5)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

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VII. CONCLUSION(a) Same number of fingers but differenct postures(b) OK sign(c) Gesture with different degree of rotations
Fig. 13. Some final results of hand gesture recognition

In this paper, we presented a hand gesture detection andrecognition method for gesture-based smart glasses
applications in the context of MPEG IoMT, which aims to
support efficient media consumption in IoT and wearable
environments. In addition, we presented a method ofrepresentation of hand contours using Bézier curves toprovide an interoperable interface between processing units
each of which perform gesture detection and gesturerecognition, respectively, in an IoMT framework.
Experimental results showed that the proposed methods ofdetection and recognition of hand gestures could be
effectively applied to wearable applications such as smart
glasses in real time. In addition, we have found that the
Bézier curve based descriptor is more appropriate to support
hand gesture based use cases than the MPEG-7 CSS and
MPEG-U Part 2 based on the comparison analysis.
ACKNOWLEDGMENT

TABLE IV

PC

Input stereo
image sequence

EXPERIMENTAL CONDITIONS
Processor: Inter® Core™ i7-6700 CPU @3.40
GHz
Memory: 16.0GB
OS: Windows 10, x64
Frame rate: 6 frames/sec
Resolution: 1280 * 720
Number of gesture type: 12 types
Number of Frame: 252 framesThis work was supported by Institute for Information &communications Technology Promotion (IITP) grant funded
by Korean Government (MSIT) (R0127-15-1015). This
paper is an extension of the ICACT 2017 conference paper.
REFERENCES[1][2]

Table 5 shows the performance of proposed method interms of recognition accuracy measured by Eg. (5). The
comparison results are for both cases of pre-recorded video
and real-time video with and without the temporal
post-processing.
Pre-recoded video are captured in an ideal environment
without illumination change and no movement of handlocation except hand gestures themselves. In such ideal
environment, we obtained the accuracy of 97.2% in arecognition of finger numbers and 95.0% of recognition of
finger position, respectively. However, in experiments on
real-time video captured by the head mounted camera,
illumination and location of hand are not stable due to a little
motion of head. As a result, the recognition accuracies are
decreased to 95.2% and 89.9%, respectively.
With the temporal post-processing, the recognition
accuracy of the number of finger and finger position using
pre-recorded video are 100.0% and 98.6%, respectively. In
the case of real-time video, the accuracies are given by 97.0%
and 93.0%, respectively. This results mean that the proposed
temporal post processing significantly enhances theperformance of recognition accuracy higher.
TABLE V
EXPERIMENTAL RESULTS OF RECOGNITION ACCURACY
Recognition accuracy
Test
Pre-recorded video
Real-time video
condition
FingerFingerFingerFingernumberposition
numberposition
Without post
97.2
95.0
95.2
89.9
processing
With post
100.0
98.6
97.0
93.0
processing

[3]
[4]
[5]
[6]
[7]
[8]

M. Mitrea, Working draft 2.0 of ISO/IEC 23093-1 IoMT Architecture,
ISO/IEC JTC1/SC29/WG11 N17094, Torino, July 2017.
H. Cheng, L. Yang, Z. Liu, “Survey on 3D Hand Gesture Recognition,”
IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 9, pp.
1659-1673, Sep. 2016.
M. Mitrea, S.-K. Kim, and S. Chun, Use cases for Internet ofMedia-Things and Wearables, ISO/IEC JTC1/SC29/WG11 N16533,
Chengdu, China, Oct 2016.
A. Yang, S. Chun, H. Ko, J. G. Kim, Hand Gesture Description for
Wearable Applications in M-IoTW, ISO/IEC JTC1/SC29/WG11
M38526, Geneva, Swiss, May. 2016.
A. Yang, S. Chun, and J.-G. Kim, “Detection and Recognition of Hand
Gesture for Wearable Applications in IoMTW,” In Proc. ICACT2017,
Feb. 2017, pp. 598 – 601.
Information technology – Rich media user interfaces – Part 2:
Advanced user interaction (AUI) interfaces, ISO/IEC 23007-2:2012,
Oct. 2012.
Information technology – Multimedia content description interface –Part 3 Visual, ISO/IEC 15938-3:2002, Oct. 2001.
D. Zhang, G. Lu, “Evaluation of MPEG-7 shape descriptors against
other descriptors,” Multimedia Systems, vol. 9, no. 1, pp. 15-30, July
2003.
Anna Yang received the MS. degree from Korea
Aerospace University, Korea, in 2017. She iscurrently working toward the Ph.D degree in the
Department of Electronics and Information
Engineering, Korea Aerospace University,
Goyang-city, Korea. Her current research interests
include IoT/wearable media applications, and
MPEG standards.

Jae-Gon Kim received the B.S. degree inelectronics engineering from Kyungpook National
University, Daegu, Korea, in 1990, the M.S. and
Ph.D degrees in electronical engineering from the
Korea Advanced Institute of Science andTechnology (KAIST), Daejeon, Korea, in 1992 and2005, respectively. From 1992 to 2007, he was with
Electronics and Telecommunications ResearchInstitute (ETRI), where he was involved in thedevelopment of digital broadcasting media services,
MPEG-7/7/21 standards and related applications, and convergence media

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

1053

technologies. From 2001 to 2002, he was a Staff Associate at the Department
of Electrical Engineering, Columbia University, New York, USA. Since
2007, he has been with the Korea Aerospace University, Goyang-si,
Gyeonggi-do, Korea, where he is currently a professor in the School ofElectronics and Information Engineering. His research interests include
digital video coding, video signaling processing, digital broadcasting media,
and multimedia applications.
Sung Moon Chun received the B. S. degree from
Sungkyunkwan University, Korea, in 1990. From1990 to 2000, he was with Hyundai Electronics,
where he was involved in the development of
HDTV, MPEG-2/4 standards and related video
codec technologies. From 2002 to 2012, he waswith ECT Inc, where he was involved in thedevelopment of Semiconductor and related stereo
video technologies. He is currently CTO in Insignal
Inc. His research interests include video
compression, virtual reality, and wearable applications.

Copyright ⓒ 2017 GiRI (Global IT Research Institute)