Facial color transition model to express char emotion

156 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011

A Facial Color Transition Model to Express Character EmotionKyu 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

As the game industry and technology rapidly grow, users demand better computer performance, higher quality graphics, and more advanced artificial intelligence for games. Such demands spur the production of games loaded with sophisticated graphics comparable to real photos. Earlier, games used to have characters composed of limited number of polygons and had to be supported by low-performance computers. While nowadays game characters appear more natural, there are still difficulties in expressing characters’ emotions in detail because computing resources should be shared with other functions such as physics calculation, scene graph management, and applying artificial intelligence.

Though many facial color studies were able to suggest the facial color models [1-3] based on actual human blood flow, pulse, or skin temperature, which expressed facial colors with increasing redness for a certain emotion, in real games and animations, these methods of varying redness proved to be inefficient in expressing wide range of facial colors of emotional states within a restricted time for games.

Other works that investigated facial changes associated with emotional expression focused on the measurement of muscle activity. The Facial Action Coding System (FACS) [4] is a comprehensive and widely used method of objectively describing facial activity. Using FACS, a

facial expression is manually coded and decomposed into the specific Action Units which are contraction or relaxation of one or more muscles. Muscle actions to express emotions are simulated by displacing or changing the control points inside the geometry of a face [5]. Limitation of these methods includes that only selected muscles have been considered and their interrelation is hard to simulate various emotional states [6,7]. Even in the simple case for fast implementation by moving major facial parts [8], such as eyebrows, eyes, cheeks, and mouth, the emotional states are expressed exaggeratively and unnaturally.

In this work, after comparing and analyzing 60 animations, we suggest a novel Facial Color Transition Model (FCTM) that expresses varying skin colors according to the strength of external stimuli. 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 traits, as opposed to previous methods that expressed emotion through blood flow, skin temperature, or interrelation of facial muscles with complicated mathematical models, which require much calculation time to simulate feelings accurately.

This paper is organized as follows. In Section 2, we explain Robert Plutchik’s psychoevolutionary theory of emotion [9], colors and emotions [10], and Eysenck’s dimensions of personality theory [11]. In Section 3,

[Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP: 203.153.32.66] || Click here to download free Android application for thisjournal

https://market.android.com/details?id=comm.app.medknow


157IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011

the FCTM based on the theory of color personality is proposed. FCTM consists of an emotion–color association and an emotional transition model based on the human personality theory for the emotional stability and the transition speed. In Section 4, the simulation results of FCTM according to the reaction of emotion–color association are evaluated. Finally, conclusions and future works are discussed in Section 5.

2. EMOTION AND PERSONALITY THEORIES

Although the definition of “emotion” may vary on the fields of study, the term is defined as “a sequence of events that starts with the occurrence of an arousing stimulus and ends with a passionate feeling” [9]. Physiological psychologist Neil R. Carlson also said, “emotion is a passive or active feeling aroused by a specific situation” [12]. Since the action and reaction situation is common in games, the emotion of a character needs to be expressed to describe the current situation.

In this section, we introduce the theories of emotion, colors, and personality, which are the bases of our transition model.

2.1 Robert Plutchik’s Psychoevolutionary Theory of Emotion

The theory of psychological evolution consists of three distinct models: the structural model, the sequential model, and the derivative model [9]. Each model has fundamentally different views and our study will focus on the structural model and the sequential model. The derivative model, which explains certain human behaviors that are seen in lower animals, is not directly 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 to an event and also operates to increase the chance

of survival. For example, when an attack or an escape has been accomplished, the relationship between an individual and environment changes; since the goal has been achieved, the emotional reaction also ceases [13]. Although it is not certain whether emotional state comes first or physiological awakening comes first, it is often said that an impulsive reaction occurs after an emotional state [12,14]. Moreover, such impulsive reaction is expressed in the form of tensing muscles, facial expression, making fists, running away, or attacking, and it tends to recover the previous emotional state, which is called the behavioral homeostatic feedback system [9]. This reaction can be applied to the emotional relief against stimulus.

2.2 Colors’ Association with Emotions

Color, along with action and language, is a crucial element in expressing emotion. The symbol of colors and how emotion is affected are examined in order to verify the influence of colors.

Color association and symbolism: Color association is the association of a specific person, an event, or an experience to a color, and symbolism is to express an abstract notion or feelings. Thus, if a common image is symbolized among many people and it gains a public acknowledgment, then it is called the symbol of a color [10]. Colors corresponding to psychological emotions are matched by combining their metaphorical notion with associated representation.

Perception of color and emotional effect: Colors have many emotional impacts, namely, temperature, strong and weak, hard and soft, and active and calm. For

Figure 1: Cross-section of multi-dimensional emotion model [8].

Park KH: A Facial Color Transition Model





hardness and softness, brightness and low saturation create a soft feeling, whereas dimness and high saturation create a hard feeling. Also, weaker contrast and saturation convey calmness as opposed to stronger contrast and saturation, which convey activeness [10].

These color association and emotional effects of colors are used as properties to express emotions for the FCTM.

2.3 Dimensions of Personality Theory

The theory of four temperaments was improved by Immanuel Kant and Wilhelm Wundt. Kant and Wundt claimed that the conventional four temperaments varied according to the two major dimensions of emotions: speed (introverted or extraverted) and intensity (stable or unstable) [11]. They claimed that in terms of emotional reaction speed, melancholic and phlegmatic temperaments have slower reaction speed compared to choleric and sanguine temperaments, and in terms of emotional intensity, melancholic and choleric temperaments are unstable compared to phlegmatic and sanguine temperaments [15], as shown in Figure 2.

The Dimensions of Personality Theory states that human emotional reaction depends on personality and trait. By applying this theory, when a character’s emotion is altered by an external factor, its results can be diversely expressed in terms of skin colors. If the emotional change were to be linear, it would not have been suitable for expressing a variety of emotions because all characters would respond to an external factor in the same manner. So, the FCTM utilizes Eysenck’s Dimensions of Personality Theory to produce various responses

depending on the character’s traits.

3. FACIAL COLOR TRANSITION MODEL

The FCTM is a combination of the facial color association obtained by analysis of 60 animations and an emotional model based on the human personality theory about the emotional stability and reaction speed.

3.1 Derivation of Representative Color from Animation Characters

The emotional and psychological effects of colors on each individual have received contributions both from personal experiences and the culture. Each color represents distinctive emotions attached to it. Red embodies excitement and passion, both positively and negatively. Blue is described as dependable and cool, and the emotional meaning of blue shows devotion, piety and sincerity. The emotional meaning associated with green is guilt, envy, and jealousy.

Emotion–color association as shown in Table 1 is drawn based on the Theory of Emotion, such as color symbolism and association for the primary and secondary emotions [9]. Its representative colors are deduced from the conventional associations in novels, design textbooks, and classical literature in Korea. Even if other nations or cultures can have different mappings, the representative colors for emotions are derived by analyzing actual animation or game contents as described below.

Since we do not have the numerical values for the 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 emotional states, we manually select a facial point and Figure 2: Personality and individual differences [10].






set ranges of red, green, and blue values of facial colors from various characters in animations. When Color (Pf) is the color of a manually picked pixel (Pf) that is included in facial area, FacialRegion is a set of pixels in a face with {P(x,y) | Color(Pf) −d< Color(P) < Color(Pf) +d for each color channel, and P is connected with Pf }, where d is a small constant value.

With this selected facial region, the procedure to get the differences of facial colors is as follows:Step 1: A facial image is selected when an emotional state of a character reaches its maximum.Step 2: Each R, G, B value is added for the facial region and the averages of color channels are obtained by dividing the number of pixels in the face.Step 3: Color difference between a normal state and an emotional state is averaged for all 60 animations.

We denote the set of animations as Q = {1(Akira), 2(Alradin), q, …, 60(PrincessMonnoke)} and the set of emotions as E = {n(Normal), a(Anger), e, …, w(Awe)}, where q or e is an instance of animation or emotion, respectively.

∆RN

x y R x yeq q nqx y Facial gion

= −∈∑1 {R ( , ) ( , )}e

( , ) Re (1)

∆ ∆RL

Re eqq

L

==∑1

1( )

(2)

In the above equations, N is the number of pixels in the FacialRegion and ∆ eqR is the red channel average difference between a normal emotion and an expressed emotion for one animation (q). L is the number of animations in the set Q, and ∆ eR is the red channel average for one emotion (e) from all animations (green and blue differences are calculated the same way as red). By Equations (1) and (2) and a set of animations, we can construct the emotion–color association as shown in Table 2.

Table 2 shows the representative emotion–color association by differences of red, green, and blue color values with changes of contrast and brightness. Colors with an asterisk (*) represent the emotions with very 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 1: Emotion–color association in literatureEmotion ColorJoy RedAcceptance GreenFear BlueSurprise YellowSadness Black/blueDisgust KhakiAnger RedAnticipation BlueLove PinkSubmission GrayAwe PurpleDisappointment BlueRemorse GrayContempt NavyAggressiveness RedOptimism Green

Table 2: Representative emotion–color association valuesEmotion Color C/B ∆R ∆G ∆BJoy * –/+Acceptance Green −27 40 −12Fear Blue −114 −55 −9Surprise * +/+Sadness * –/–Disgust Khaki −81 −19 −5Anger Red 11 −48 −43Anticipation * +/+Love Pink −26 −62 −47Submission * –/–Awe Purple −27 18 98Disappointment * –/–Remorse * –/–Contempt Navy −16 10 92Aggressiveness Red 80 −18 −45Optimism * +/+

C/B means contrast/brightness. Plus sign, minus sign, and asterisk represent increasing, decreasing, and meaningless variation, respectively






one. It indicates how many values have to be changed from the normal state to a certain emotion given in Table 1.

Though in the facial muscle expression there are unique, highly recognizable, and pan-cultural facial emotions [17], colors and emotions do not have one-to-one mapping because of racial or cultural differences. There are also dual mappings that become hard to interpret. For example, red cheeks could mean shame or love, but a fully red face could mean anger or aggressiveness. In this work, however, we find some salient mappings that are commonly described and understood within bounded nations (Korea and Japan) by analyzing animations, and use the mapped colors as features for emotions presented on the entire face. For other nations or cultures, different mappings with Table 2 can be investigated and used for the emotional color representation.

3.2 Modelling of Emotional Transition with Personality Dimension

Diverse changes in emotion would not be apparent if the change of emotion is to be linear. Thus, we design an emotion-reaction function that supports such diverse changes according to the character’s personality and trait.

The model of emotional changes according to personality consists of four parts: homeostatic value, calculation of accumulated stimulus, emotionally intensity weights for stable and unstable traits, and emotional transition functions for introverted and extraverted traits. These are based on Eysenck’s study of four major features of traits, which are related to reaction speed and transition intensit Homeostatic value: Homeostasis is a trait that wants to maintain equilibrium, which means that an emotional state tends to return normal state after the relaxation of excitement. If a character is affected by a certain stimulus Ii and no additional stimulation occurs, homeostatic value H is subtracted from Ii repeatedly 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.

Hh

i =

0, if there is a stimulus at time i, 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 and Hi as shown in Equation (3)

S S I H wi i i i← + − ×−max{ , ( ) }0 1 (3)

where Ii is current input stimulus, Hi is homeostatic value at i time, and w is an emotional intensity weight.

Figure 4 illustrates the steps to calculate the accumulated stimulus. Figure 4(a) shows input stimuli with times and Figure 4(b) depicts homeostatic values with or without inputs. In Figure 4(c), accumulated stimuli are calculated by inputs and homeostatic values.

Emotional intensity weight: According to Dimensions of Personality Theory [11], melancholic and choleric temperaments are unstable compared to phlegmatic and sanguine temperaments. In other words, with the same stimulus, the melancholic and choleric traits show much more changes in emotion compared to the other two traits. The emotionally unstable type has a relatively large value for w in Equation (3) and for the stable type w is set to a small value.

Emotional transition function (FI for introverted temperament and FE for extraverted temperament): The transition speed responds obtusely to introverted types while responding sensitively to extraverted types. Melancholic and phlegmatic temperaments have slower transition speed compared to choleric and sanguine temperaments. Such responses can be best described by two functions: an exponential function and a logarithmic function. Figure 5(a) shows a slow change in emotion due to low sensitivity; on the other hand, with high sensitivity, Figure 5(b) shows a rapid change with values of Si using Equations (4) and (5):

F S eI iSi p( ) ( / )= (4)

F S SE i e ip( ) log ( )= (5)

where p is a regulation value and it is assigned 21, which shows natural and symmetric emotional transition within the range of possible variation.

The value of red for an emotional state (Rei) at i-th time with an input (Ii) can be calculated by adding the normal

Figure 4: Calculation of accumulated stimulus in FCTM: (a) input stimulus, (b) homeostatic value, (c) accumulated stimulus.

(a) (b) (c)






red value (Rn) to the difference (∆ eR ) in Table 2 with the portion of changes F Si( ) in Equation (6).

R R R F S Rei n e i ei= + × < <∆ ( ), if 0 255 (6)

The transition function F(Si) is normalized to have a value in [0, 1], which is denoted by F Si( ). The normalized transition function can be F SI i( ) in the case of an introvert type and F SE i( ) when it is an extravert type. Green and blue channels are calculated in the same way as red.

4. VERIFYING THE FACIAL COLOR TRANSITION MODEL

In this work, we derived the emotion–color association and suggest the FCTM based on the Dimensions of Personality Theory about four temperaments (melancholic, phlegmatic, choleric, and sanguine), which is modeled by the emotional transition functions for the reaction speed (introverted or extraverted) and by the emotional intensity weight for the emotional stability (unstable or stable). Four temperaments can be simulated differently by adjusting the properties of the emotional reaction functions and the emotional intensity weights.

In this section, we evaluate the effectiveness of the model

for the color variation and the reaction speed. In the experiments d for FacialRegion is 10, and w in Equation (3) is set by 1 for the stable trait and 2 for the unstable trait. Though some emotional colors can be expressed in facial regions instead of the entire face, since the size of a game character’s face is small, a specified color is used for the entire face and we calculate representative colors from entire faces as we consider the entire face of a character as the target to express emotions.

Figure 6 shows the facial color transition images for a melancholic trait character. After inputting sequence of stimuli, the acceptance emotion is expressed by the FCTM. Eventually, as time progresses without any stimuli, the facial color reverts to its normal state.

Figure 7 shows examples of a 3D character’s face processed by the FCTM. The expression of an emotion is achieved by simply changing the colors of pixels on the texture image according to the stimulated time. So, the method is easy and fast to apply to real-time applications.

4.1 Emotional Color Evaluation

Usually, the emotional change of a character is rarely presented in a game. On the contrary, in animation, most of the character’s emotions are signaled by changes in facial color, which are designed by artists. We evaluate

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


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

(a) (b)





Table 3: Facial color expression in animation contentsNo. Animation

contentsEmotion Main

colorNumber of

stimuliFacial color

(R,G,B)A1 Macross zero Acceptance Green 1 145, 182,

131A2 Beauty and the

beastFear Blue 1 54, 57, 72

A3 Akira Disgust Khaki 1 56, 55, 46A4 Akira Anger Red 3 135, 65, 35A5 Only yesterday Love Pink 3 184, 141,

134A6 The prince of

EgyptAwe Purple 3 39, 42, 71

A7 Jubei Ninpucho Ninja

Contempt Navy 1 96, 93, 143

A8 Macross zero Aggressiveness Red 3 238, 154, 113

Table 4: Facial color expression by FCTM for melancholic traitsNo. Strength of

stimulusEmotion Number of

stimuliFacial color

(R,G,B)F1 (100) Acceptance 1 124, 164, 108F2 (100) Fear 1 67, 70, 88F3 (100) Disgust 1 100, 100, 81F4 (50), (72), (86) Anger 3 132, 53, 22F5 (50), (72), (86) Love 3 178, 132, 124F6 (50), (72), (86) Awe 3 153, 122, 144F7 (100) Contempt 1 84, 78, 135F8 (50), (72), (86) Aggressiveness 3 233, 107, 76

the effectiveness of the FCTM by comparing the emotional colors of FCTM with those of characters from popular animations.

Tables 3 and 4 show facial colors for eight emotions with the corresponding animation contents and FCTM simulations, respectively. The number of stimuli in Table 3 is counted manually for each emotion from the emotional circumstances in animation contents. The strength of stimulus in Table 4 presents the number of inputs with the estimated strength to accomplish the final excitement. In the case of “F4”, there are three stimulus inputs with strength of 50, 72, and 86 to mimic emotional circumstances as given in Table 3.

Final facial colors for the emotions in both tables are different because the FCTM uses the average difference between one of exited states and the normal state, while the colors of normal states in each animation are varied. However, the ratios of red, green, and blue channels for every emotion are very similar in both the tables as shown in Figure 8.

Figure 8 shows the comparison of colors between animation contents and the simulations of FCTM by color percentage. The label “Ax” represents the color ratio of an emotion from animation contents and “Fx” means the color ratio of a simulation from FCTM. When a character with melancholic temperament is excited

Figure 7: Examples of FCTM 3D Character: (a) anger, (b) acceptance, (c) awe, and (d) normal emotional states.

Figure 8: Color ratios of animation contents and FCTM for eight emotions.

by one or several stimuli from the environment and its transition of facial color is simulated by FCTM (Fx) or animated by an artist (Ax), absolute color values from the simulation may be different from the corresponding animation, but the percentage of color channels is very similar in both cases, which means that facial colors are different in brightness but they are similar enough to present emotions in hue. In the cases of “A6” and “F6” (the blue color portion of “A6” is significantly larger than that of “F6” to present the emotion of “awe”), since three inputs are stimulated and the blue color of normal state in the animation is originally set by a lower value, the distribution of colors is somewhat different. Except some cases, FCTM can simulate emotional colors relatively similar to the created colors in animations.

4.2 Transition Speed Evaluation

We compare changes in RGB values between an original animation sequence and an FCTM sequence. After detecting the region of a face by the skin color range, we compute the average colors in each channel from the extracted face, and then we depict the average values of red, green, and blue channels according to frames for the original animation sequence and for the FCTM sequence. Finally, we compute the difference between the colors of transitions in each sequence as shown in Figure 9.






Figure 9 shows the comparison between two temperaments: (a) melancholic and (b) choleric temperaments for FCTM sequences. The standard deviation of error for melancholic temperament is 3.88 and for choleric temperament it is 9.13. Results show that the melancholic temperament is more similar than the choleric temperament to the animation character, and the FCTM can simulate the transition speed of the original animation by altering the trait property with little error.

5. CONCLUSION

Despite improved graphics and smarter intelligence, emotions of game character are still expressed insufficiently. Moreover, the currently used physiological variance of redness is too limited to express a wide range of emotions in games and animations. To address this limitation, we suggest the FCTM that consists of the emotion–color association obtained by the analysis of 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 express emotions and demonstrate the effectiveness of emotional transitions for various temperaments.

Furthermore, the application of the FCTM to 2D textures of 3D model proves that it can directly be used for real-time applications. The FCTM can provide more improved reality and immersion to games by expressing the character’s emotion and the FCTM can be used not only in the game industry, but also other fields requiring the expression of emotions as well.

Future studies will focus on an emotional method to represent regional changes in a face for larger characters, and we modify FCTM for commercial applications.

6. ACKNOWLEDGMENT

This study was supported by the MKE under the HNRC-ITRC support program supervised by the NIPA (NIPA-2010-C1090-1011-0010) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0021892).

REFERENCES

1. T Yamada and T Watanabe, “Effects of facial color on virtual facial image synthesis for dynamic facial color and expression under


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

(a) (b)





laughing emotion,” Robot and Human Interactive Communication, IEEE International Workshop on ROMAN, pp. 341-6, 2004.

2. P Kalra and N M Thalmann, “Modeling of vascular expressions in facial animation,” Proceedings of Computer Animation, pp. 50-8, 1994.

3. P Buddharaju, I T Pavlidis, and P Tsiamyrtzis, “Physiology-based face recognition,” Advanced Video and Signal Based Surveillance, IEEE Conference on AVSS, pp. 354-9, 2005.

4. P Ekman, and W Friesen, “Facial Action Coding System: A Technique for the Measurement of Facial Movement,” Palo Alto: Consulting Psychologists Press; 1978.

5. P Kalra, N M Thalmann, L Moccozet, G Sannier, A Aubel, and D Thalmann, “Real-time animation of realistic virtual humans,” Computer Graphics and Applications, vol.18, no.5, pp. 42-56, 1998.

6. A M Aniss and P S Sachdev, “Concordance between surface and intra-muscular recordings of facial EMG during emotional expression,” Electromyography and Clinical Neurophysiology, vol. 36, pp.73-9, 1996.

7. C G Kohler, T Turner, N M Stolar, W B Bilker, C M Brensinger, R E Gur et al, “Differences in facial expressions of four universal emotions,” Psychiatry Research, vol.128, pp.235-44, 2004.

8. K Kurihara, D Sugiyama, S Matsumoto, N Nishiuchi, and K Masuda, “Facial emotion and gesture reproduction method for substitute

robot of remote person,” Computers and Industrial Engineering, vol. 56, pp. 631-47, 2009.

9. R Plutchik, “Emotions and Life,” American Psychological Association, pp. 102-11, 2003.

10. F Birren, “Color Psychology and Color Therapy,” Kessinger Publishing, LLC, 2006.

11. H J Eysenck and S B G Eysenck, “The Biological Basis of Personality,” Transaction Publishers, pp. 34-5, 2006.

12. N. R. Carlson, Physiology of Behavior, Allyn and Bacon, 2004.13. L S Greenberg and S C Paivio, “Working with emotions in

psychotherapy,” New York: Guilford Press; 1997.14. R Plutchik, “Emotions in the practice of psychotherapy,” American

Psychological Association, 2000.15. H J Eysenck and S B G Eysenck, “Psychoticism as a Dimension of

Personality,” University of London Press: London, Hodder and Stoughton, 1976.

16. P Kakumanu, S Makrogiannis, and N Bourbakis, “A survey of skin-color modeling and detection methods,” Pattern Recognition, vol. 40, pp. 1106-22, 2007.

17. P Ekman and W Friesen, “A new pan-cultural facial expression of emotion,” Motivation and Emotion, vol. 10, pp. 159-68, 1986.

AUTHORSKyu Ho Park received his M.S. degree in Advanced Imaging Engineering at Chung-Ang University, Seoul, Korea. Currently, he is working toward the Ph.D. degree in Advanced Imaging Engineering at Chung-Ang University. His research interests include in Computer Game, Image Processing, and Human-computer Interaction.

E-mail: [email protected]

Seung-Ho Shin received his B.S. degree from In-Ha University, M.S degree in Media Tech. from Sogang University, and Ph.D. in Advanced Imaging Science at Chung-Ang University, Seoul, Korea. Currently, he is a technical manager for mobile DTV in SK Telecom Corporation, Seoul, Korea. His research interests include AV Codec techniques (H.264/AVC, MPEG-2 and 4), 3DTV,

Computer Vision, and Digital Image Processing.


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


KyuSik Chang received his M.S. degree in Computer Engineering at SangMyung University, Seoul, Korea. Currently, he is working toward the Ph.D. degree in Advanced Imaging Engineering at Chung-Ang University. His research interests include in Computer Music, Sound Processing, and Sound Synthesing.


Tae Yong Kim (Corresponding Author) received his B.S. degree in Electrical Engineering and M.S. degree in Communication Engineering from Han-Yang University, Seoul, Korea, in 1986 and 1988, respectively, and Ph.D. in Computer Science and Engineering from Pohang University of Science & Technology, Korea, in 1998. Currently, he is a professor at the Graduate School of

Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Korea from 2003. His research interests include in Computer Vision, Image Processing, and A.I.





Facial color transition model to express char emotion

Education

Transcript of Facial color transition model to express char emotion