Facial color transition model to express char emotion


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Facial color transition model to express char emotion

  1. 1. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal 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, 156 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011
  2. 2. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal 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]. IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011 157
  3. 3. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model 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 158 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011
  4. 4. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model 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 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011 159
  5. 5. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model 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. 160 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011
  6. 6. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model 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. IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011 161
  7. 7. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model 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. 162 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011
  8. 8. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model (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 REFERENCES express emotions and demonstrate the effectiveness 1. T Yamada and T Watanabe, “Effects of facial color on virtual facial of emotional transitions for various temperaments. image synthesis for dynamic facial color and expression under IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011 163
  9. 9. [Downloaded free from http://www.jr.ietejournals.org on Monday, December 31, 2012, IP:]  ||  Click here to download free Android application for thisjournal Park KH: A Facial Color Transition Model laughing emotion,” Robot and Human Interactive Communication, robot of remote person,” Computers and Industrial Engineering, vol. IEEE International Workshop on ROMAN, pp. 341-6, 2004. 56, pp. 631-47, 2009. 2. P Kalra and N M Thalmann, “Modeling of vascular expressions in facial 9. R Plutchik, “Emotions and Life,” American Psychological Association, animation,” Proceedings of Computer Animation, pp. 50-8, 1994. pp. 102-11, 2003. 3. P Buddharaju, I T Pavlidis, and P Tsiamyrtzis, “Physiology-based face 10. F Birren, “Color Psychology and Color Therapy,” Kessinger Publishing, recognition,” Advanced Video and Signal Based Surveillance, IEEE LLC, 2006. Conference on AVSS, pp. 354-9, 2005. 11. H J Eysenck and S B G Eysenck, “The Biological Basis of Personality,” 4. P Ekman, and W Friesen, “Facial Action Coding System: A Technique Transaction Publishers, pp. 34-5, 2006. for the Measurement of Facial Movement,” Palo Alto: Consulting 12. N. R. Carlson, Physiology of Behavior, Allyn and Bacon, 2004. Psychologists Press; 1978. 13. L S Greenberg and S C Paivio, “Working with emotions in 5. P Kalra, N M Thalmann, L Moccozet, G Sannier, A Aubel, and psychotherapy,” New York: Guilford Press; 1997. D Thalmann, “Real-time animation of realistic virtual humans,” Computer Graphics and Applications, vol.18, no.5, pp. 42-56, 1998. 14. R Plutchik, “Emotions in the practice of psychotherapy,” American Psychological Association, 2000. 6. A M Aniss and P S Sachdev, “Concordance between surface and intra- muscular recordings of facial EMG during emotional expression,” 15. H J Eysenck and S B G Eysenck, “Psychoticism as a Dimension of Electromyography and Clinical Neurophysiology, vol. 36, pp.73-9, Personality,” University of London Press: London, Hodder and 1996. Stoughton, 1976. 7. C G Kohler, T Turner, N M Stolar, W B Bilker, C M Brensinger, R E Gur 16. P Kakumanu, S Makrogiannis, and N Bourbakis, “A survey of skin- et al, “Differences in facial expressions of four universal emotions,” color modeling and detection methods,” Pattern Recognition, vol. Psychiatry Research, vol.128, pp.235-44, 2004. 40, pp. 1106-22, 2007. 8. K Kurihara, D Sugiyama, S Matsumoto, N Nishiuchi, and K Masuda, 17. P Ekman and W Friesen, “A new pan-cultural facial expression of “Facial emotion and gesture reproduction method for substitute emotion,” Motivation and Emotion, vol. 10, pp. 159-68, 1986. 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 164 IETE JOURNAL OF RESEARCH | VOL 57 | ISSUE 2 | MAR-APR 2011