Facial color transition model to express char emotion

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A Facial Color Transition Model to
Express Character Emotion
Kyu Ho Park, Seung-Ho Shin, KyuSik Chang and Tae Yong Kim1
GSAIM, Chung-Ang University, 221 Heukseok-Dong, Dongjak-Gu, Seoul, Republic of Korea

ABSTRACT
High quality graphics for game characters has been continuously improving, spurred by the astonishing growth of
the graphics technology. Despite such improvements, the current expression of emotion has limited representation
because it is difficult to implement it in real-time and a large amount of storage is required to store sprites for various
feelings. Since users are demanding a more expressive character to reflect emotion, such restrictions can prevent
the users from getting fully indulged in a game. To address this, we propose a facial color transition model, which
is a combination of the emotional colors based on the theory of emotion, the emotion–color association, and the
emotional transition with personal traits. The model is implemented by using the homeostatic value, the accumulated
stimulus, and nonlinear transition functions, which support diverse changes according to the character’s personality
with low computational cost. The reflection of the game character’s emotion on its facial color will not only make
users immerse into the game, but also enrich their fantasy in games.

Keywords:
Color transition model, Emotional colors, Emotion expression, Emotion–color association, Facial color, Game character.

1. INTRODUCTION facial expression is manually coded and decomposed
into the specific Action Units which are contraction
As the game industry and technology rapidly grow, users or relaxation of one or more muscles. Muscle actions
demand better computer performance, higher quality to express emotions are simulated by displacing or
graphics, and more advanced artificial intelligence for changing the control points inside the geometry of
games. Such demands spur the production of games a face [5]. Limitation of these methods includes that
loaded with sophisticated graphics comparable to only selected muscles have been considered and their
real photos. Earlier, games used to have characters interrelation is hard to simulate various emotional states
composed of limited number of polygons and had to [6,7]. Even in the simple case for fast implementation by
be supported by low-performance computers. While moving major facial parts [8], such as eyebrows, eyes,
nowadays game characters appear more natural, there cheeks, and mouth, the emotional states are expressed
are still difficulties in expressing characters’ emotions exaggeratively and unnaturally.
in detail because computing resources should be shared
with other functions such as physics calculation, scene In this work, after comparing and analyzing 60
graph management, and applying artificial intelligence. animations, we suggest a novel Facial Color Transition
Model (FCTM) that expresses varying skin colors
Though many facial color studies were able to suggest according to the strength of external stimuli. The
the facial color models [1-3] based on actual human blood model is implemented by using the homeostatic value,
flow, pulse, or skin temperature, which expressed facial the accumulated stimulus, and nonlinear transition
colors with increasing redness for a certain emotion, in functions, which support diverse changes according to
real games and animations, these methods of varying the character’s traits, as opposed to previous methods
redness proved to be inefficient in expressing wide range that expressed emotion through blood flow, skin
of facial colors of emotional states within a restricted temperature, or interrelation of facial muscles with
time for games. complicated mathematical models, which require much
calculation time to simulate feelings accurately.
Other works that investigated facial changes associated
with emotional expression focused on the measurement This paper is organized as follows. In Section 2, we
of muscle activity. The Facial Action Coding System explain Robert Plutchik’s psychoevolutionary theory
(FACS) [4] is a comprehensive and widely used method of emotion [9], colors and emotions [10], and Eysenck’s
of objectively describing facial activity. Using FACS, a dimensions of personality theory [11]. In Section 3,

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Park KH: A Facial Color Transition Model

the FCTM based on the theory of color personality of survival. For example, when an attack or an escape
is proposed. FCTM consists of an emotion–color has been accomplished, the relationship between an
association and an emotional transition model based on individual and environment changes; since the goal has
the human personality theory for the emotional stability been achieved, the emotional reaction also ceases [13].
and the transition speed. In Section 4, the simulation Although it is not certain whether emotional state comes
results of FCTM according to the reaction of emotion– first or physiological awakening comes first, it is often
color association are evaluated. Finally, conclusions and said that an impulsive reaction occurs after an emotional
future works are discussed in Section 5. state [12,14]. Moreover, such impulsive reaction
is expressed in the form of tensing muscles, facial
2. EMOTION AND PERSONALITY THEORIES expression, making fists, running away, or attacking, and
it tends to recover the previous emotional state, which
Although the definition of “emotion” may vary on the is called the behavioral homeostatic feedback system
fields of study, the term is defined as “a sequence of [9]. This reaction can be applied to the emotional relief
events that starts with the occurrence of an arousing against stimulus.
stimulus and ends with a passionate feeling” [9].
Physiological psychologist Neil R. Carlson also said, 2.2 Colors’ Association with Emotions
“emotion is a passive or active feeling aroused by a
specific situation” [12]. Since the action and reaction Color, along with action and language, is a crucial
situation is common in games, the emotion of a character element in expressing emotion. The symbol of colors and
needs to be expressed to describe the current situation. how emotion is affected are examined in order to verify
the influence of colors.
In this section, we introduce the theories of emotion,
colors, and personality, which are the bases of our Color association and symbolism: Color association
transition model. is the association of a specific person, an event, or an
experience to a color, and symbolism is to express an
2.1 Robert Plutchik’s Psychoevolutionary Theory of abstract notion or feelings. Thus, if a common image is
Emotion symbolized among many people and it gains a public
acknowledgment, then it is called the symbol of a
The theory of psychological evolution consists of three color [10]. Colors corresponding to psychological
distinct models: the structural model, the sequential emotions are matched by combining their metaphorical
model, and the derivative model [9]. Each model has notion with associated representation.
fundamentally different views and our study will focus
on the structural model and the sequential model. Perception of color and emotional effect: Colors have
The derivative model, which explains certain human many emotional impacts, namely, temperature, strong
behaviors that are seen in lower animals, is not directly and weak, hard and soft, and active and calm. For
related to the human feelings and it is not used in this
paper.

The practical use of the psychoevolutionary theory allows
the categorization of emotions expressible in characters,
and the relationship of emotions corresponding to
representative colors will be composed.

Structural model (primary emotion and secondary
emotion): Similar to the three primary colors, Robert
Plutchik stated that human emotions consist of eight
primary emotions (Joy, Trust, Fear, Surprise, Sadness,
Disgust, Anger, and Anticipation) and other emotions
can be combined by these eight primary emotions. The
emotions outside the circle represent combinations of
two adjacent primary emotions that are called secondary
emotions, as shown in Figure 1. Each primary emotion
shows a medium level of intensity.

Sequential model (system of active equilibrium
feedback): Emotion provides a feedback of one’s reaction Figure 1: Cross-section of multi-dimensional emotion
to an event and also operates to increase the chance model [8].

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hardness and softness, brightness and low saturation depending on the character’s traits.
create a soft feeling, whereas dimness and high
saturation create a hard feeling. Also, weaker contrast 3. FACIAL COLOR TRANSITION MODEL
and saturation convey calmness as opposed to stronger
contrast and saturation, which convey activeness [10]. The FCTM is a combination of the facial color association
obtained by analysis of 60 animations and an emotional
These color association and emotional effects of colors model based on the human personality theory about the
are used as properties to express emotions for the FCTM. emotional stability and reaction speed.

2.3 Dimensions of Personality Theory 3.1 Derivation of Representative Color from Animation
Characters
The theory of four temperaments was improved by
Immanuel Kant and Wilhelm Wundt. Kant and Wundt The emotional and psychological effects of colors
claimed that the conventional four temperaments varied on each individual have received contributions both
according to the two major dimensions of emotions: from personal experiences and the culture. Each color
speed (introverted or extraverted) and intensity represents distinctive emotions attached to it. Red
(stable or unstable) [11]. They claimed that in terms of embodies excitement and passion, both positively and
emotional reaction speed, melancholic and phlegmatic negatively. Blue is described as dependable and cool, and
temperaments have slower reaction speed compared the emotional meaning of blue shows devotion, piety and
to choleric and sanguine temperaments, and in terms sincerity. The emotional meaning associated with green
of emotional intensity, melancholic and choleric is guilt, envy, and jealousy.
temperaments are unstable compared to phlegmatic
and sanguine temperaments [15], as shown in Figure 2. Emotion–color association as shown in Table 1 is drawn
based on the Theory of Emotion, such as color symbolism
The Dimensions of Personality Theory states that human and association for the primary and secondary emotions
emotional reaction depends on personality and trait. [9]. Its representative colors are deduced from the
By applying this theory, when a character’s emotion is conventional associations in novels, design textbooks,
altered by an external factor, its results can be diversely and classical literature in Korea. Even if other nations or
expressed in terms of skin colors. If the emotional change cultures can have different mappings, the representative
were to be linear, it would not have been suitable for colors for emotions are derived by analyzing actual
expressing a variety of emotions because all characters animation or game contents as described below.
would respond to an external factor in the same
manner. So, the FCTM utilizes Eysenck’s Dimensions Since we do not have the numerical values for the
of Personality Theory to produce various responses representative emotional colors, we analyze the
character’s facial colors of emotions that are painted by
artists in popular animations. During the analysis of
animation sequences, the emotion–color relationship
can be detected by observing the changes of facial colors.
Figure 3 shows the sequences of images that represent
the changes in facial colors of two different emotional
transition situations.

The facial colors of characters in the source animations
are measured to find each representative color given in
Table 1. The measure is calculated by comparing the
character’s excited state with normal state. Natural skin
color image, excluding hair, eyebrows, eyes, teeth, lips,
and shadow, are extracted for comparison. There are
many works to detect facial area automatically using
color information, which can be a challenging task since
the facial color is affected by various factors such as
illumination, background, and ethnicity [16]. Especially,
many of the existing methods are not effective when face
color varies frequently with emotions or it is exaggerated
to depict emotions. Thus, since we focus on the color
difference between the normal state and one of excited
Figure 2: Personality and individual differences [10]. emotional states, we manually select a facial point and


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set ranges of red, green, and blue values of facial colors 1
from various characters in animations. When Color (Pf) is ∆Req = ∑ {Re q(x , y ) − Rnq(x , y )}
N ( x , y )∈Facial Re gion
(1)
the color of a manually picked pixel (Pf) that is included
in facial area, FacialRegion is a set of pixels in a face with 1 L
{P(x,y) | Color(Pf) −d< Color(P) < Color(Pf) +d for each ∆Re = ∑ (∆Req )
L q=1
(2)
color channel, and P is connected with Pf }, where d is a
small constant value.
In the above equations, N is the number of pixels in
With this selected facial region, the procedure to get the the FacialRegion and ∆Req is the red channel average
differences of facial colors is as follows: difference between a normal emotion and an expressed
Step 1: A facial image is selected when an emotional state emotion for one animation (q). L is the number of
of a character reaches its maximum. animations in the set Q, and ∆Re is the red channel
Step 2: Each R, G, B value is added for the facial region average for one emotion (e) from all animations (green
and the averages of color channels are obtained by and blue differences are calculated the same way as red).
dividing the number of pixels in the face. By Equations (1) and (2) and a set of animations, we
Step 3: Color difference between a normal state and an can construct the emotion–color association as shown
emotional state is averaged for all 60 animations. in Table 2.

We denote the set of animations as Q = {1(Akira), Table 2 shows the representative emotion–color
2(Alradin), q, …, 60(PrincessMonnoke)} and the set of association by differences of red, green, and blue color
emotions as E = {n(Normal), a(Anger), e, …, w(Awe)}, values with changes of contrast and brightness. Colors
where q or e is an instance of animation or emotion, with an asterisk (*) represent the emotions with very
respectively. small changes from their initial state to the emotional

Figure 3: Facial colors with emotional transition; normal to anger transition (top sequence) and acceptance to aggressiveness
transition (bottom sequence).
Table 2: Representative emotion–color association values
Emotion Color C/B ∆R ∆G ∆B
Table 1: Emotion–color association in literature
Joy * –/+
Emotion Color
Acceptance Green −27 40 −12
Joy Red
Fear Blue −114 −55 −9
Acceptance Green
Surprise * +/+
Fear Blue
Sadness * –/–
Surprise Yellow
Disgust Khaki −81 −19 −5
Sadness Black/blue
Anger Red 11 −48 −43
Disgust Khaki
Anticipation * +/+
Anger Red
Love Pink −26 −62 −47
Anticipation Blue
Submission * –/–
Love Pink
Awe Purple −27 18 98
Submission Gray
Disappointment * –/–
Awe Purple
Remorse * –/–
Disappointment Blue
Contempt Navy −16 10 92
Remorse Gray
Aggressiveness Red 80 −18 −45
Contempt Navy
Optimism * +/+
Aggressiveness Red
Optimism Green C/B means contrast/brightness. Plus sign, minus sign, and asterisk represent
increasing, decreasing, and meaningless variation, respectively


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one. It indicates how many values have to be changed where Ii is current input stimulus, Hi is homeostatic value
from the normal state to a certain emotion given in at i time, and w is an emotional intensity weight.
Table 1.
Figure 4 illustrates the steps to calculate the accumulated
Though in the facial muscle expression there are stimulus. Figure 4(a) shows input stimuli with times and
unique, highly recognizable, and pan-cultural facial Figure 4(b) depicts homeostatic values with or without
emotions [17], colors and emotions do not have one-to- inputs. In Figure 4(c), accumulated stimuli are calculated
one mapping because of racial or cultural differences. by inputs and homeostatic values.
There are also dual mappings that become hard to
interpret. For example, red cheeks could mean shame Emotional intensity weight: According to Dimensions
or love, but a fully red face could mean anger or of Personality Theory [11], melancholic and choleric
aggressiveness. In this work, however, we find some temperaments are unstable compared to phlegmatic
and sanguine temperaments. In other words, with the
salient mappings that are commonly described and
same stimulus, the melancholic and choleric traits show
understood within bounded nations (Korea and Japan)
much more changes in emotion compared to the other
by analyzing animations, and use the mapped colors
two traits. The emotionally unstable type has a relatively
as features for emotions presented on the entire face.
large value for w in Equation (3) and for the stable type
For other nations or cultures, different mappings with w is set to a small value.
Table 2 can be investigated and used for the emotional
color representation. Emotional transition function (F I for introverted
temperament and FE for extraverted temperament):
3.2 Modelling of Emotional Transition with Personality The transition speed responds obtusely to introverted
Dimension types while responding sensitively to extraverted types.
Melancholic and phlegmatic temperaments have slower
Diverse changes in emotion would not be apparent if
transition speed compared to choleric and sanguine
the change of emotion is to be linear. Thus, we design
temperaments. Such responses can be best described by
an emotion-reaction function that supports such diverse
two functions: an exponential function and a logarithmic
changes according to the character’s personality and trait.
function. Figure 5(a) shows a slow change in emotion
due to low sensitivity; on the other hand, with high
The model of emotional changes according to personality
sensitivity, Figure 5(b) shows a rapid change with values
consists of four parts: homeostatic value, calculation of
of Si using Equations (4) and (5):
accumulated stimulus, emotionally intensity weights
for stable and unstable traits, and emotional transition
functions for introverted and extraverted traits. These FI(Si ) = e( Si /p ) (4)
are based on Eysenck’s study of four major features of
traits, which are related to reaction speed and transition FE(Si ) = log e (Sip ) (5)
intensit Homeostatic value: Homeostasis is a trait that
where p is a regulation value and it is assigned 21, which
wants to maintain equilibrium, which means that an shows natural and symmetric emotional transition
emotional state tends to return normal state after the within the range of possible variation.
relaxation of excitement. If a character is affected by a
certain stimulus Ii and no additional stimulation occurs, The value of red for an emotional state (Rei) at i-th time
homeostatic value H is subtracted from Ii repeatedly with an input (Ii) can be calculated by adding the normal
until the state reaches the normal state. Though H can be
varied according to circumstances, we use H as a constant
value for the purpose of simulation.

0, if there is a stimulus at time i
Hi = 
h , otherwise

Calculation of accumulated stimulus: If a character is
excited with Ii, accumulated stimulus (Si) is computed
by adding previous Si-1 with the difference between Ii (a) (b) (c)
and Hi as shown in Equation (3)
Figure 4: Calculation of accumulated stimulus in FCTM:
(a) input stimulus, (b) homeostatic value, (c) accumulated
Si ← max{0 , Si − 1 + ( Ii − Hi ) × w} (3) stimulus.


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red value (Rn) to the difference ( ∆Re ) in Table 2 with the for the color variation and the reaction speed. In the

portion of changes F(Si ) in Equation (6). experiments d for FacialRegion is 10, and w in Equation
(3) is set by 1 for the stable trait and 2 for the unstable

Rei = Rn + ∆Re × F(Si ), if 0 < Rei < 255 (6) trait. Though some emotional colors can be expressed
in facial regions instead of the entire face, since the size
The transition function F(Si) is normalized to have of a game character’s face is small, a specified color is
a value in [0, 1], which is denoted by F(Si ). The used for the entire face and we calculate representative
normalized transition function can be FI  (Si ) in the case colors from entire faces as we consider the entire face of
 a character as the target to express emotions.
of an introvert type and FE (Si ) when it is an extravert
type. Green and blue channels are calculated in the
Figure 6 shows the facial color transition images for a
same way as red.
melancholic trait character. After inputting sequence
of stimuli, the acceptance emotion is expressed by the
4. VERIFYING THE FACIAL COLOR TRANSITION FCTM. Eventually, as time progresses without any
MODEL stimuli, the facial color reverts to its normal state.
In this work, we derived the emotion–color association
Figure 7 shows examples of a 3D character’s face
and suggest the FCTM based on the Dimensions
processed by the FCTM. The expression of an emotion is
of Personality Theory about four temperaments
achieved by simply changing the colors of pixels on the
(melancholic, phlegmatic, choleric, and sanguine), which
texture image according to the stimulated time. So, the
is modeled by the emotional transition functions for the
method is easy and fast to apply to real-time applications.
reaction speed (introverted or extraverted) and by the
emotional intensity weight for the emotional stability 4.1 Emotional Color Evaluation
(unstable or stable). Four temperaments can be simulated
differently by adjusting the properties of the emotional Usually, the emotional change of a character is rarely
reaction functions and the emotional intensity weights. presented in a game. On the contrary, in animation, most
of the character’s emotions are signaled by changes in
In this section, we evaluate the effectiveness of the model facial color, which are designed by artists. We evaluate

(a) (b)

Figure 5: Emotional transition functions: (a) function for introverted types and (b) function for extraverted types.

Figure 6: A 2D example of FCTM for melancholic trait and acceptance emotion.


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the effectiveness of the FCTM by comparing the by one or several stimuli from the environment and its
emotional colors of FCTM with those of characters from transition of facial color is simulated by FCTM (Fx) or
popular animations. animated by an artist (Ax), absolute color values from
the simulation may be different from the corresponding
Tables 3 and 4 show facial colors for eight emotions animation, but the percentage of color channels is very
with the corresponding animation contents and FCTM similar in both cases, which means that facial colors are
simulations, respectively. The number of stimuli in different in brightness but they are similar enough to
Table 3 is counted manually for each emotion from the present emotions in hue. In the cases of “A6” and “F6”
emotional circumstances in animation contents. The (the blue color portion of “A6” is significantly larger than
strength of stimulus in Table 4 presents the number of that of “F6” to present the emotion of “awe”), since three
inputs with the estimated strength to accomplish the final inputs are stimulated and the blue color of normal state
excitement. In the case of “F4”, there are three stimulus in the animation is originally set by a lower value, the
inputs with strength of 50, 72, and 86 to mimic emotional distribution of colors is somewhat different. Except some
circumstances as given in Table 3. cases, FCTM can simulate emotional colors relatively
similar to the created colors in animations.
Final facial colors for the emotions in both tables are
different because the FCTM uses the average difference 4.2 Transition Speed Evaluation
between one of exited states and the normal state, while We compare changes in RGB values between an original
the colors of normal states in each animation are varied. animation sequence and an FCTM sequence. After
However, the ratios of red, green, and blue channels detecting the region of a face by the skin color range,
for every emotion are very similar in both the tables as we compute the average colors in each channel from the
shown in Figure 8. extracted face, and then we depict the average values of
red, green, and blue channels according to frames for the
Figure 8 shows the comparison of colors between original animation sequence and for the FCTM sequence.
animation contents and the simulations of FCTM by Finally, we compute the difference between the colors of
color percentage. The label “Ax” represents the color transitions in each sequence as shown in Figure 9.
ratio of an emotion from animation contents and “Fx”
means the color ratio of a simulation from FCTM. When Table 3: Facial color expression in animation contents
a character with melancholic temperament is excited No. Animation Emotion Main Number of Facial color
contents color stimuli (R,G,B)
A1 Macross zero Acceptance Green 1 145, 182,
131
A2 Beauty and the Fear Blue 1 54, 57, 72
beast
A3 Akira Disgust Khaki 1 56, 55, 46
A4 Akira Anger Red 3 135, 65, 35
A5 Only yesterday Love Pink 3 184, 141,
134
A6 The prince of Awe Purple 3 39, 42, 71
Egypt
Figure 7: Examples of FCTM 3D Character: (a) anger, (b) A7 Jubei Ninpucho Contempt Navy 1 96, 93, 143
acceptance, (c) awe, and (d) normal emotional states. Ninja
A8 Macross zero Aggressiveness Red 3 238, 154,
113

Table 4: Facial color expression by FCTM for melancholic
traits
No. Strength of Emotion Number of Facial color
stimulus stimuli (R,G,B)
F1 (100) Acceptance 1 124, 164, 108
F2 (100) Fear 1 67, 70, 88
F3 (100) Disgust 1 100, 100, 81
F4 (50), (72), (86) Anger 3 132, 53, 22
F5 (50), (72), (86) Love 3 178, 132, 124
F6 (50), (72), (86) Awe 3 153, 122, 144
F7 (100) Contempt 1 84, 78, 135
Figure 8: Color ratios of animation contents and FCTM for
F8 (50), (72), (86) Aggressiveness 3 233, 107, 76
eight emotions.


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(a) (b)
Figure 9: Color changes by anger emotion: (a) melancholic temperament with error and (b) choleric temperament with
error.

Figure 9 shows the comparison between two Furthermore, the application of the FCTM to 2D textures
temperaments: (a) melancholic and (b) choleric of 3D model proves that it can directly be used for
temperaments for FCTM sequences. The standard real-time applications. The FCTM can provide more
deviation of error for melancholic temperament is 3.88 improved reality and immersion to games by expressing
and for choleric temperament it is 9.13. Results show that the character’s emotion and the FCTM can be used not
the melancholic temperament is more similar than the only in the game industry, but also other fields requiring
choleric temperament to the animation character, and the the expression of emotions as well.
FCTM can simulate the transition speed of the original
animation by altering the trait property with little error. Future studies will focus on an emotional method to
represent regional changes in a face for larger characters,
5. CONCLUSION and we modify FCTM for commercial applications.

Despite improved graphics and smarter intelligence, 6. ACKNOWLEDGMENT
emotions of game character are still expressed
insufficiently. Moreover, the currently used physiological This study was supported by the MKE under the HNRC-
variance of redness is too limited to express a wide range ITRC support program supervised by the NIPA (NIPA-
of emotions in games and animations. To address this 2010-C1090-1011-0010) and Basic Science Research Program
limitation, we suggest the FCTM that consists of the through the National Research Foundation of Korea (NRF)
funded by the Ministry of Education, Science and Technology
emotion–color association obtained by the analysis of
(2010-0021892).
60 animations and the emotional transition model based
on the emotional stability and the transition speed. The
experimental results support the usability of colors to
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AUTHORS
Kyu Ho Park received his M.S. degree in Advanced KyuSik Chang received his M.S. degree in Computer
Imaging Engineering at Chung-Ang University, Seoul, Engineering at SangMyung University, Seoul, Korea.
Korea. Currently, he is working toward the Ph.D. Currently, he is working toward the Ph.D. degree in
degree in Advanced Imaging Engineering at Chung-Ang Advanced Imaging Engineering at Chung-Ang University.
University. His research interests include in Computer His research interests include in Computer Music, Sound
Game, Image Processing, and Human-computer Processing, and Sound Synthesing.
Interaction.
E-mail: multimidi@multimidi.co.kr
E-mail: oracle1027@wm.cau.ac.kr
Tae Yong Kim (Corresponding Author) received his B.S.
Seung-Ho Shin received his B.S. degree from In-Ha degree in Electrical Engineering and M.S. degree in
University, M.S degree in Media Tech. from Sogang Communication Engineering from Han-Yang University,
University, and Ph.D. in Advanced Imaging Science Seoul, Korea, in 1986 and 1988, respectively, and Ph.D.
at Chung-Ang University, Seoul, Korea. Currently, he in Computer Science and Engineering from Pohang
is a technical manager for mobile DTV in SK Telecom University of Science & Technology, Korea, in 1998.
Corporation, Seoul, Korea. His research interests include Currently, he is a professor at the Graduate School of
AV Codec techniques (H.264/AVC, MPEG-2 and 4), 3DTV, Advanced Imaging Science, Multimedia and Film, Chung-Ang University,
Computer Vision, and Digital Image Processing. Seoul, Korea from 2003. His research interests include in Computer Vision,
Image Processing, and A.I.
E-mail: shin5693@hanmail.net
E-mail: kimty@cau.ac.kr

DOI: 10.4103/0377-2063.81746; Paper No. JR 525_09; Copyright © 2011 by the IETE


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