Design facial appearance for roles in video games

(1)

Design facial appearance for roles in video games

Shang Hwa Hsu

*

_{, Ching-Han Kao, Muh-Cherng Wu}

Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Ta Hsueh Road, HsinChu 30010, Taiwan, ROC

a r t i c l e

i n f o

Keywords: Video game Role Fuzzy AHP BP neural network

a b s t r a c t

Roles in video games often serve as avatars of players. Different game players may have their particular preferences on a role’s facial appearance. It would be desirable to allow players to customize the design of roles. This paper presents two methods for recommending a roles’ facial appearance for a particular game player and illustrates the two methods by using heroic roles as an example. The two recommendation methods are designated as the text-input and the picture-input approaches. The text-input approach requests the game player to carry out pairwise comparisons for determining the relative weights of 16 personality traits of heroes. The recommendation mechanism for the text-input approach is based on the fuzzy AHP (analytic hierarchy process). Whereas the picture-input approach requests the game player to view a sample set of pictures and rate his/her preferences on each picture. The recommendation mech-anism for the picture-input approach is based on the BP (back-propagation) neural network. Experiments indicated that the text-input approach is more effective in terms of recommending an appropriate facial appearance, yet at the expense of needing more user time.

1. Introduction

In playing a video game (called a game hereafter), a player ex-presses his or her intentions by manipulating the actions of a role. A game role is a character, which serves as the player’s avatar or competitor. Roles to a game are as important as actors to a ﬁlm. Casting appropriate actors can lead to the success of a ﬁlm. In the same way, designing appropriate roles is very important to the success of a video game.

Previous researchers have published numerous studies on the role design of video games. Most of them attempted to create a life-like role by emulating human behaviors—such as dialogue (Brusk & Eladhari, 2006; Gustafson, Boye, Fredriksson, Johanneson, & Königsmann, 2005; Jan & Traum, 2005), intelligence (Frasca, 2001; Lair & Duchi, 2000; Vala, Paiva, & Prada, 2004), motor-skill

actions (Blumberg & Galyean, 1997), and emotional expressions

(Rizzo, Neumann, Enciso, Fidaleo, & Noh, 2001; Wallraven, Breidt, Cunningham, & Bülthoff, 2005).

Of these studies, those which analyze emotional expressions attempt to automatically create a facial expression to represent a character’s mood (e.g. happy, angry, sad, and disgusted). Such fa-cial expression studies have an implicit objective—mood

manifesta-tion (Bartlett, Hager, Ekman, & Sejnowski, 1999; Ekman, 1993;

Zhang & Ji, 2005). That is, a computer-generated facial expression should model a particular mood (e.g. sad) that is easily recogniz-able by humans.

In a film, proper recognition of an actors’ mood by interpreting their body languages is never enough. To produce a popular film, the attractive appearance of actors is often much more important. Likewise, the appearance of a game role may influence player involvement in the game. An attractive appearance may induce players to have affectionate feelings for the avatar, and in turn,

make game play more fun (Hsu, Lee, & Wu, 2005). Even though

the facial appearance of a role is very important to game design, this topic has rarely been investigated.

This study proposes two methods for automatically recom-mending attractive facial appearances of heroic roles to game play-ers—in a customization manner. The observations in this study indicate that different game players have different preferences for the appearance of a hero. According to further interviews, this difference is due to the fact that players have different preferences for a hero’s personality traits. This implies that personality traits may be manifested by facial appearances.

Based on this implication, this study proposes a research frame-work to design a hero’s facial appearance as favored by a particular

game player (Fig. 1). This research framework involves two phases:

creation and application. The creation phase develops a database that relates facial appearance to personality traits, which involves two steps. Firstly, multimedia software is used to create samples of her-oes’ facial appearances. Secondly, game players evaluate the personal-ity traits for each facial appearance. In this phase, a vector comprising numeric data can model both a facial appearance and its personality traits. The created database is called a face-trait database.

doi:10.1016/j.eswa.2008.05.049

* Corresponding author. Tel.: +886 3 5726731; fax: +886 3 5722392. E-mail address:shhsu@cc.nctu.edu.tw(S.H. Hsu).

Contents lists available atScienceDirect

Expert Systems with Applications

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a

(2)

a highly favored heroes’ facial appearance can be quickly recom-mended from the face-trait database. Two recommendation mech-anisms can be used to retrieve these highly favored facial appearance proﬁles.

One mechanism is a text-input approach, which requires the game player to determine each personality trait’s relative weight.

This mechanism uses the fuzzy AHP technique (Laarhoven &

Ped-rycz, 1983; Saaty, 1980; Shamsuzzaman, 2000; Zadeh, 1975). The

other mechanism is a picture-input approach, which requires the game player to evaluate their degree of preferences for a sample set of pictures (facial appearances). This mechanism is

imple-mented using the back-propagation (BP) neural network

technique.

The remainder of this paper is organized as follows. Section2

describes how the face-trait database is created. Section 3 ﬁrst

introduces the fuzzy AHP technique, and then presents the

text-input recommendation mechanism. Section 4 describes the

picture-input recommendation mechanism. Section 5 presents

the experiments and results for justifying the effectiveness of the two recommendation mechanisms. The last section contains concluding remarks.

2. Face-trait database

A prototype face-trait database has been developed. In develop-ing the database, heroes’ facial appearances were created by com-mercially available multimedia software (Live Studio Head Tool V2.6). We use 16 personality traits of heroes to characterize each fa-cial appearance created. A hero’s fafa-cial appearance could then be encoded by a vector consisting of 16 elements, which provides a key for the recommendation facial appearances.

2.1. Facial appearance creation

The multimedia software used to create a facial appearance is based on a feature-based approach. That is, the conﬁguration of a face is composed of several features. A feature may denote a part of a face such as eyes, lips, and nose or denote a face’s characteristic such as skin color and texture. For each feature, there are many op-tions for selection. Various combinaop-tions of feature opop-tions result in different facial appearances.

For features associated with geometric shapes, their feature

op-tions are created by the parametric-geometry approach (Myung &

Han, 2001; Verroust, Schonek, & Roller, 1992). That is, changing some of its geometric parameters can vary the shape of an object. Considering an object with rectangular shape as an example, we could take the length-to-width ratio as one parameter and vary shape of the object by changing the parameter value. Likewise, a facial feature such as eyes can be modeled by a parametric-geom-etry shape with more than one parameter; for example, the curva-ture of upper eye lid and that of lower eye lid. By varying these two parameters, we could have many different shapes for modeling eyes.

Example features associated with geometric shapes include lips, noses, eyes, eyebrows, and facial outline. These shapes are com-monly controlled by a set of parameters, and each parameter value Text-input recommendation

mechanism (Fuzzy AHP)

Picture-input recommendation mechanism (Neural network) Establish

face-trait database

Creation phase Application phase

Fig. 1. Research framework.

(3)

is a real number in a predeﬁned range such as [0, 1]. The input of such a real-number value is through a scale-bar input device

(rightmost part ofFig. 2).

Due to the completeness property of real numbers, there are theoretically an inﬁnite number of options available for features associated with geometric types. By contrast, some other features provide only a ﬁnite number of feature options; for example—type of hair and type of moustache. The input of such a nominal-type feature option is through the clicking of a menu box (middle part ofFig. 2).

The multimedia software we used provides 12 features. In this research, only ﬁve shape-oriented features are chosen as variables in designing a facial appearance for the prototype database. These ﬁve features are facial outline, eye brows, eyes, lip, and nose, which are selected because most prior research has concluded their

importance on facial expression and recognition (Adolphs, 2002;

Brunelli & Poggio, 1993; Sadrô, Jarudi, & Sinha, 2003).

The prototype database which is essentially expandable, at its present state, includes 243 different facial appearances. That is, each of the ﬁve facial features has only three options for selection,

which leads to 35_{= 243 different facial appearances. Three}

in-stances of the facial appearances are shown inFig. 3.

2.2. Personality traits evaluation

We use 16 personality traits to characterize each facial appear-ance. These personality traits are a hero’s characters reported in

a prior research byHsu, Kao, and Wu (2007). According to their

re-search, by the technique of factor analysis (principal components

factoring) (McDonald, 1985), these 16 personality traits can be

cat-egorized into three groups—bravery, visionary, and moral as shown inFig. 4.

In the characterization of a facial appearance, a ﬁve-point scale evaluates each personality trait. The higher the value, the higher degree does the facial appearance reveal—in terms of the personal-ity trait. In the characterization process, we asked 112 subjects (61males, 51 females at the age of 17–25) to evaluate the 16

per-sonality traits for each of 243 facial appearances. All these subjects are all game player, who are either senior high school or college students.

Results of the characterization process yield 243 trait vectors, each of which represents a particular facial appearance. The value of each element, ranging from 1 to 5, is the mean score reported by

all the subjects. Let X = [x1, . . ., x16] denote a trait vector so obtained,

which is further transformed into a normalized vector, denoted by

Y ¼ ½y1; . . . ;y16 where yi¼

xi

P16

k¼1xk

andP16i¼1yi¼ 1.

3. Text-input recommendation mechanism

To create a hero face favored by a particular game player, it is important to know how the player weights each personality trait. The procedure for determining the weights of personality traits is

by applying the fuzzy AHP technique (Saaty, 1990). The technique,

widely applied in various areas (Durán & Aguilo, 2007; Kim & Yoon,

1992; Mamaghani, 2002; Muralidar & Santhanam, 1990; Wu, Lo, & Hsu, 2007) is brieﬂy stated below.

Firstly, the 16 personality traits are hierarchically clustered as

shown inFig. 4. This hierarchy indicates that these 16 personality

traits, based on a prior research (Hsu et al., 2007), can be

catego-rized into three groups—bravery, visionary, and moral, which are called group-level traits. Each personality traits within a group is called a member-level trait.

Secondly, the relative weights for group-level traits and that for member-level traits in each group have to be determined. We there-fore have four weight-determination problems, one for group-level and three for member-level traits. To each weight-determination problem, we asked the game player to carry out a pairwise com-parison experiment, and use a fuzzy AHP algorithm to process the experiment data for determining the relative weights.

Thirdly, we use the relative weights to compute a recommenda-tion-priority value to retrieve a hero face from the face-trait data-base for the game player.

3.1. Pairwise comparison experiment

The pairwise comparison experiment is explained by using the weight-determination of group-level traits as an example. Of the three group-level traits, any two (a pair) is chosen for comparison. Assume the pair (bravery, visionary) is chosen. Then, the player is asked to answer the following question: ‘‘Consider a person to be recognized as a hero. Of the two groups of personality traits, which one is more important? Try to compare the degree of importance between them.”

Now suppose the player answers bravery is more important

than visionary. Then, ﬁve linguistic variables listed inTable 1are

provided for the player to express his/her opinion. These linguistic variables range from ‘‘absolutely important” to ‘‘equally impor-tant” on a ﬁve level scales, where between any two consecutive Fig. 3. Example of facial appearances.

Hero

Bravery Visionary Moral

Courageous Cool Mighty Charismatic Unbeatable Daring Ambitious Visionary Persevering Calm Trustworthy Capable Leadership Gracious Unselfish Just

(4)

scales an intermediate scale is additionally deﬁned so that nine

scales are ﬁnally created. As shown inTable 1, each linguistic

var-iable is represented by a triangular fuzzy number—for example, ~

7 ¼ ð6; 7; 8Þ denotes ‘‘very strongly important”. The adoption of lin-guistic variables is to resolve the vagueness occurred in human

judgment (Zadeh, 1975).

Based on the pairwise comparison experiment, a n n matrix

Ã = [ãij] could be obtained, where n denotes the number of traits

to be compared, ~ A ¼ 1 ~a12 ~a1n ~ a21 1 ~a2n .. . .. . . . . .. . ~ an1 ~an2 1 2 6 6 6 6 4 3 7 7 7 7 5

Notice that ãij= 1/ãji, which ensures that the comparison for each

pair (i, j) is consistent; and ãii= 1, which denotes that a

self-compari-son is always ‘‘equally important” and is not needed. 3.2. Fuzzy AHP algorithm

Two procedures are used to obtain the relative weights for group-level and member-level traits. The ﬁrst one Compute_Rela-tive_Weight is intended to compute the relative weights, and the second one Validity_Check_for_Relative_Weights is developed for checking the validity of the obtained relative weights. These two procedures are respectively described below, where the deﬁnitions of arithmetic operators (i.e., , , and Defuzzy) are introduced in theAppendix.

3.2.1. Procedure Compute_Relative_Weight

Step 1: Calculate ~Wi, the fuzzy weight for each row i in Ã (Buckley,

1985) ~

Zi¼ ð~ai1 ~ai2 ~ainÞ1n_;

₈

_{i ¼ 1; 2; . . . ; n}

~

Wi¼ ~Zi ð~Z1 ~Z2 ~ZnÞ1;

8

i ¼ 1; :::; n

Step 2: Defuzzication of ~Wiand Ã (Teng & Tzeng, 1993)

^

Wi¼ Defuzzyð ~WiÞ

aij¼ Defuzzyð~aijÞ; whereA ¼ ½aij and ~A ¼ ½~aij Step 3: Compute relative weights W_{i} Wi¼ Wi^ Pn i¼1 ^ Wi 3.2.2. Procedure Validity_Check_for_Relative_Weights Step 1: Compute W i W 1 W2 .. . W n 2 6 6 6 6 4 3 7 7 7 7 5¼ A W1 W2 .. . Wn 2 6 6 6 6 4 3 7 7 7 7 5

Step 2: Compute kmax, the maximum eigenvalue

kmax¼1 n W 1 W1 þ W 2 W2 þ þ W n Wn

Step 3: Compute CI, the consistency index CI ¼kmax n

n 1 Step 4: Compute CR

CR = CI/RI the values of RI are shown inTable 2(Saaty, 1980).

Step 5: Consistency check

If CR 6 0.1 (pairwise comparison data Ã is reasonably consistent)

Output the resulting relative weights Wi(1 6 i 6 n)

Else (CR > 0.1 pairwise comparison data Ã are inconsistent) Repeat the pairwise comparison experiment.

Endif

3.3. Recommending mechanism

To recommend a hero face favored by a particular game player, we ﬁrst compute a recommendation-priority value for each facial appearance in the face-trait database and recommend the one with the highest recommendation-priority value.

Deﬁne S = {(Yi, Fi)|1 6 i 6 n} as the face-trait database developed

for facial design, where Fi denotes ith facial appearance, and

Yi= [yij], 1 6 j 6 16 denotes the personality-trait vector of FiNotice

that Fiis a 2D picture while Yiis a vector with 16 numeric elements,

where yijdenotes the degree of jth personality traits that picture Fi

manifest to a ‘‘common people”. Let W = [wj], 1 6 j 6 16 represent

the preferences of a particular game player on each of the 16

per-sonality traits, where wjdenotes the relative weight of jth

person-ality trait, perceived by the game player—an ‘‘individual people” rather than a ‘‘common people”.

The procedure for recommending facial appearances most favorable to a particular game player proceeds as follows.

Step 1: Compute the recommendation-priority value pi¼

X16 j¼1

wj yij; 1 6 i 6 n

Step 2: Output the recommended one i¼ arg maxðpiÞ

4. Picture-input recommendation mechanism

The BP neural network technique has been widely used as a

pre-dictor (Dutta & Shekhar, 1988; Liau & Chen, 2005; O’Leary, 1998;

Salchenberger, Cinar, & Lash, 1992; Tam & Kiang, 1992). Given a sample set of input/output data obtained from a real-world sys-tem, we can use the technique to establish a BP network that serves as an input/output mapping mechanism—a three-layer

architecture as shown inFig. 5. The established BP network can

be used to predict the output for a new input data set. Details of

the BP neural network technique can be referred toWasserman

(1989) andHertz, Krogh, and Palmer (1991).

The picture-input recommendation mechanism is to establish a BP network for a particular game player. For such a BP network, its Table 1

Linguistic variables used in the fuzzy AHP

Fuzzy number Linguistic variables

~ 1 ¼ ð1; 1; 1Þ Equally important ~ 3 ¼ ð2; 3; 4Þ Weakly important ~ 5 ¼ ð4; 5; 6Þ Essentially important ~

7 ¼ ð6; 7; 8Þ Very strongly important

~

9 ¼ ð8; 9; 10Þ Absolutely important

~

2 ¼ ð1; 2; 3Þ; ~4 ¼ ð3; 4; 5Þ; ~6 ¼ ð5; 6; 7Þ; ~8 ¼ ð7; 8; 9Þ Intermediate values between two adjacent judgments

Table 2 Values of RI

n 1 2 3 4 5 6 7 8 9 10 11

(5)

input is a facial appearance denoted by Fi= [fik], 1 6 i 6 243,

1 6 k 6 15, where fik(a binary number) denotes the existence of

the kth feature option in the ith facial appearance. As stated, a facial appearance has ﬁve features, each of which has three options for selection. This leads to 15 (5 3) types of feature options. A facial appearance can then be modeled by a vector with 15 binary ele-ments, in which 1 denotes a feature option exists and 0 denotes its nonexistence. The output of such a BP network is denoted by

Pi, which denotes the game player’s preference on the ith facial

appearance.

The use of the BP neural network technique involves two phases: training and predicting phases. In the training phase, we at-tempt to establish a BP network for the game player. Out of the 243 facial appearances, we randomly sample 81 ones and use them to build up a BP network. The algorithms of the training process can

be referred toGrossberg (1974) and Rumelhart, Hinton, and

Wil-liams (1986). In the predicting phase, we attempt to use the estab-lished BP network to predict the player’s preferences on the other facial appearances not considered in training phase. That is, the BP network will predict the player’s preference on the remaining 162 facial appearances.

5. Experiments

An experiment is carried out to compare the performance of the text-input and the picture-input approaches. Twenty game players (10 males, 10 females at the age of 17–25) are invited as experi-ment subjects, and 243 facial appearances are created in the face-trait database. The experiment proceeds as follows.

Firstly, we carried out the text-input approach in which each subject i is requested to perform a pairwise comparison for deter-mining his/her relative weights on the 16 personality traits. The

system will output a picture (say, Xi) with the highest

recommen-dation-priority value.

Secondly, we carried out the picture-input approach in which each subject i is requested to view 81 pictures and give his/her preference on each picture. The input/output data of the 81 pic-tures are used to establish a BP network. Then, all the 243 picpic-tures

are fed into the BP network, and the one (say, Y

i) with the highest

preference value will be recommended.

Finally, each subject is requested to view the remaining 162 tures and give his/her preference on each one. Out of the 243

pic-tures, the one with the highest preference (say, Z

i) is selected.

Deﬁne rðP

iÞ as the preference of picture P evaluated by subject i.

The effectiveness of the text-input approach is measured by the

distribution of the metric: xi¼ rðZiÞ rðXiÞ, and that for the

pic-ture-input approach is measured by the distribution of y_i¼

rðZ

iÞ rðY

iÞ .

The distributions of xiand yiare shown inTable 3. The table

indicates that the text-input approach is superior to the picture-in-put approach. In the text-inpicture-in-put approach, 85% game players (17 out of the 20 subjects) will get their most favor picture, while only 35% can get so in the picture-input approach. However, the text-input approach needs more user time. The average time for performing a pairwise comparison is about 26 min. and that for evaluating 81 pictures is about 2.6 min.

The experiment results lead to the following implication. For a video game equipped with relatively few numbers of pictures, we would suggest an exhaustive display of pictures to a game player. In contrast, for a video game equipped with a great amount of pictures, we would suggest the use of the text-input approach.

6. Conclusions

Playing video games through manipulating a role is quite com-mon. Role design therefore has been a signiﬁcant research issue. Most prior research attempted to create a life-like role by develop software for emulating human’s capabilities, such as dialogues, intelligence, motor-skill, and emotional expressions. This research is unique in providing a customized facial appearance for each game player. A video game with such a customization function would become more popular.

Two methods for providing such a customization function have been developed. One is called the text-input approach whose mechanism is based on the fuzzy AHP technique. The other is called the picture-input approach whose mechanism is based on the BP neural network technique. The text-input approach requires a game player to perform a pairwise comparison on 16 personality traits. The picture-input approach requires a game player to view and evaluate a sample set of pictures.

Experiment results indicated that the text-input approach is superior to the picture-input approach, in terms of recommending an appropriate picture to a game player; however, at the price of needing more user input time.

Appendix. Arithmetical operators for fuzzy numbers

The fuzzy arithmetic operators used in this research are deﬁned

below (Laarhoven & Pedrycz, 1983; Zadeh, 1975), by referring two

fuzzy numbers ã1= (l1, m1, r1) and ã2= (l1, m1, r1).

(1) Addition operator: ã1 ã2= (l1+ l2, m1+ m2, r1+ r2)

(2) Multiplication operator: ã1 ã2= (l1 l2, m1 m2, r1 r2)

(3) Defuzzication operator (Teng & Tzeng, 1993) Defuzzy(ã1) =

|(r1 l1) + (m1 l1)|/3 + l1

Input Hidden Output

fi1 fi2 fi3 fi4 fi15 Pi

Fig. 5. Architecture of the neural network.

Table 3

Comparing effectiveness

Subjects 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Text-input xi 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0

(6)

References

Adolphs, R. (2002). Recognizing emotions from facial expressions: Psychological and neurological mechanisms. Behavioral and Cognitive Neuroscience Reviews 1, 1, 21–61.

Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J. (1999). Measuring facial expressions by computer image analysis. Psychophysiology, 36, 253–263. Blumberg, B., & Galyean, T. (1997). Multi-level control for animated autonomous

agents: Do the right thing. . . oh, not that. . .. In: R. Trappl & P. Petta (Eds.), Creating personalities for synthetic actors. Berlin: Springer-Verlag. (pp. 74–82). Brunelli, R., & Poggio, T. (1993). Face recognition features versus templates. IEEE

Transactions on PAMI, 15(10), 1042–1052.

Brusk, J., & Eladhari, M. (2006). Playing the character. RPG seminar, Tampere, March (pp. 30–31).

Buckley, J. J. (1985). Ranking alternatives using fuzzy numbers. Fuzzy Sets and Systems, 15(1), 21–31.

Durán, O., & Aguilo, J. (2007). Computer-aided machine-tool selection based on a Fuzzy-AHP approach. Expert Systems with Applications. doi:10.1016/ j.eswa.2007.01.046.

Dutta, S., & Shekhar, S. (1988). Bond rating: A non-conservative application of neural networks. In Proceedings of the IEEE international conference on neural networks (pp. 443–450).

Ekman, P. (1993). Facial expressions and emotion. American Psychologist, 48, 384–392.

Frasca, G. (2001). Rethinking agency and immersion: Videogames as a means of consciousness-raisin (essay). SIGGRAPH 2001 N-Space art gallery.

Grossberg, S. (1974). Classical and instrumental learning by neural networks. Progress in theoretical biology (Vol. 3, pp. 51–141). New York, NY: Academic Press.

Gustafson, J., Boye, J., Fredriksson, M., Johanneson, L., & Königsmann, J. (2005). Providing computer game characters with conversational abilities. In Proceedings of intelligent virtual agent (IVA05), Kos, Greece.

Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computation. Redwood City, CA:: Addison-Wesley.

Hsu, S. H., Kao, C. H., & Wu, M. C. (2007). Factors inﬂuencing player preferences for heroic roles in role-playing games. CyberPsychology & Behavior, 10(2), 293–295. Hsu, S. H., Lee, F. L., & Wu, M. C. (2005). Designing action games for appealing to

buyers. CyberPsychology & Behavior, 8(6), 585–591.

Jan, D., Traum, D. R. (2005). Dialog simulation for background characters. In IVA (pp. 65–74).

Kim, C. S., & Yoon, Y. (1992). Selection of a good expert system shell for instructional purposes in business. Information & Management, 23(5), 249–262.

Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(3), 229–241.

Lair, J. E., & Duchi, J. C. (2000). Creating human-like synthetic characters with multiple skill levels: A case study using the Soar Quakebot. AAAI 2000 fall symposium on simulating human agent, technical report FS-00-03 (pp. 75–79). AAAI Press.

Liau, L. C. K., & Chen, B. S. C. (2005). Process optimization of gold stud bump manufacturing using artiﬁcial neural networks. Expert Systems with Applications, 29, 264–271.

Mamaghani, F. (2002). Evaluation and selection of an antivirus and content ﬁltering software. Information Management & Computer Security, 10(1), 28–32. McDonald, R. (1985). Factor analysis and related techniques. Hillsdale, NJ: Lawrence

Erlbaum.

Muralidar, K., & Santhanam, R. (1990). Using the analytic hierarchy process for information system project selection. Information & Management, 18(1), 87–95. Myung, S., & Han, S. (2001). Knowledge-base parametric design of mechanical products based on conﬁguration design method. Expert Systems with Applications, 21, 99–107.

O’Leary, D. E. (1998). Using neural networks to predict corporate failure. International Journal of Intelligent Systems in Accounting, Finance and Management, 7, 187–197.

Rizzo, A. A., Neumann, U., Enciso, R., Fidaleo, D., & Noh, J. Y. (2001). Performance-driven facial animation: Basic research on human judgments of emotional state in facial avatars. CyberPsychology & Behavior, 4(4), 471–487.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representation by error propagation. Parallel and Distributed Processing, 1, 318–362.

Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw-Hill (p. 20).

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.

Sadrô, J., Jarudi, I., & Sinha, P. (2003). The role of eyebrows in face recognition. Perception, 32, 285–293.

Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23(4), 899–916.

Shamsuzzaman, M. (2000). Selection of a FMS based on fuzzy set theory and AHP methods. ISE-Thesis, Asian Institute of Technology, Bangkok.

Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38(7), 926–947. Teng, J. Y., & Tzeng, G. H. (1993). Transportation investment project selection with

fuzzy multi-objective. Transportation Planning and Technology, 17, 91–112. Vala, M., Paiva, A., & Prada, R. (2004). From motion control to emotion inﬂuence:

Controlling autonomous synthetic characters in a computer game. In AAMAS’04, New York, USA (pp. 1300–1301).

Verroust, A., Schonek, F., & Roller, D. (1992). Rule-oriented method for parameterized computer-aided design. Computer Aided Design, 24(10), 531– 540.

Wallraven, C., Breidt, M., Cunningham, D. W., & Bülthoff, H. H. (2005). Psychophysical evaluation of animated facial expressions. In Proceedings of the 2nd symposium on Applied perception in graphics and visualization (pp. 26–28). Wasserman, R. (1989). Neural computing: Theory and practice. New York, NY: Van

Nostrand Reinhold.

Wu, M. C., Lo, Y. F., & Hsu, S. H. (2007). A fuzzy CBR technique for generating product ideas. Expert Systems with Application, 34, 530–540.

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, Part 1, 8, 199–249; Part 2, 8, pp. 301–357; Part 3, pp. 43–80.

Zhang, Y., & Ji, Q. (2005). Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 699–714.