A review of Facial Caricature Generator

8/6/2019 A review of Facial Caricature Generator

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JOURNAL OF COMPUTING, VOLUME 3, ISSUE 5, MAY 2011, ISSN 2151-9617

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A review of Facial Caricature GeneratorSuriati Bte Sadimon, Mohd Shahrizal Sunar, and Habibollah Haron

Abstract Caricature is a pictorial description of a person or subject in a summarizing way using exaggeration of the most distinguish features and over-simplification of the common features in order to make that subject unique and to preserve the recognizable likeness of the subject. Facial caricature gen-

erator is developed to assist the user in producing facial caricature automatically or semi-automatically. It is derived from the rapid advance in computergraphics and computer vision as well as introduced as a part of non-photorealistic rendering technologies. Recently, facial caricature generator becomes

particularly interesting research topic due to the advantageous features of privacy, security, simplification, amusement and their rampant emergent real-

world application such as in magazine, digital entertainment, Internet and mobile application. This paper reviews the uses of caricature in variety of applica-

tions, theories and rules in the art of drawing caricature, how these theories are simulated in the development of caricature generation system and the current

research trend in this field. There are two main categories of facial caricature generator based on their input data type: human centered approach and image

centered approach. It also briefly explains the general process of generating caricature. The state of the art techniques in generating caricature are describedin detail by classifying it into four approaches: interactive, regularity-based, learning-based and predefined database of caricature illustration. Expressive

caricature is also introduced which is evolved from the neutral caricature. This paper also discusses relevant issues, problems and several promising direc-

tions for future research.

Index Termscaricature, exaggeration, face image, facial feature extraction, computer graphics and vision.

1 INTRODUCTION

aricature is a pictorial or literary representation of aperson or thing by exaggerating the most distinctivefeatures and simplifies the features that are more

common in order to make the person or thing differentfrom others and to create an easily identifiable visuallikeness. Facial caricature represents features of an indi-vidual face in a simple and effective way. It exhibits theextraordinary characteristics of a person. Caricature canbe drawn in a humorous, comical, laughable, insulting oroffensive style depending on their purpose.The word caricature is often mistaken for portrait andcartoon drawing. Portrait is any artistic representation ofa person, which the intent is to display the likeness, per-sonality and the mood of the person. A cartoon is a pieceof art, usually humorous or satirical in intent. The propor-tions of the facial features in caricature are exaggeratedand the style is much simple whereas the exaggerationdoes not exist in portrait drawing. The proportion of thefacial features in portrait drawing must be exactly thesame as the subject. The picture in caricature should looklike a real person even though it is exaggerated but incartoon, the picture does not refer to any particular realperson or the person might be not exit in the real world. Ifsomeone known by the viewer is seen in a cartoon, thecartoon becomes a caricature. So, a caricature can beviewed as a combination between portrait drawing and

cartoon drawing. However, a caricature does not neces-sarily have to be a cartoon. It could be a painting, or a

sculpture. As long as the proportions are incorrect, and itcan be recognizable as a particular person then it could beseen as a caricature [1].

A digitalcaricature is a caricature drawn using the ar-tistic skills with the help of computer software like CorelPainter, Flash, Photoshop, and Illustrator. It is drawncompletely by freehand directly onto the computer withthe use of a tablet, an electronic pencil and a mouse.Computers can really aid the process of drawing carica-ture in making it all a bit quicker and easier. However,some artists prefer to draw caricature in traditional andnatural way using pencil and paper and then scan thecaricature into the computer. This is called scanned digi-

tal caricature. On the other hand, facial caricature gener-ator is to produce facial caricature automatically or semi-automatically using computer graphics techniques. Thereare very few software programs designed specifically forautomatically creating caricatures, and they are still notreally successful [2] . Facial caricature generator attemptsto imitate the artist in drawing caricatures. They try toconvert the process of drawing caricature into the formu-la and algorithm that will be executed by computer. It canassist the user in producing caricatures whether for theskilled user or for those who do not have any ability indrawing caricature. Therefore, this paper provides acomprehensive and critical review of the recent research

activity, approach and application in facial caricaturegenerator. This paper is organized as follows: in section2, we briefly survey the application of caricature. Section3 gives a brief review of the theories and rules in the art ofdrawing caricature, including the element of caricatureand the process of drawing caricature. Category of facialcaricature generator based on their input data type is de-scribed in section 4 and the general process of generatingfacial caricature is detailed explained in section 5. In sec-tion 6, we provide a detail review of different approaches

C

Suriati Sadimon, Department of Computer Graphics and Multimedia,Universiti Tekonologi Malaysia, Johor, Malaysia.

Mohd Shahrizal Sunar, Department of Computer Graphics and Multime-dia, Universiti Tekonologi Malaysia,Johor, Malaysia.

Habibollah Harun, Department of Industrial Computing, UniversitiTekonologi Malaysia, Johor, Malaysia.

2011 Journal of Computing Press, NY, USA, ISSN 2151-9617

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in generating facial caricature from face image. Section 7briefly addresses the expressive caricature. In Section 8,we discuss several issues, problems and the promisingdirection for facial caricature generator research. Lastly,section 9 summarizes and concludes this review paper

2 APPLICATION OF CARICATURE

2.1 Caricature in Entertainment and Political Magazine

Caricature has been widely used in our daily life since thepast few decades. It always can be seen in magazine ornewspaper for various different purposes. Caricatures ofmovie stars are often displayed in entertainment maga-zines. It is used for entertainment through the combineduse of fantasy and humor. Caricature is also used for ex-pression of social and political perspective such as criti-cizing intolerance, injustice, political corruption and so-cial evils using humor and satire. Caricatures of politi-cians are commonly met in editorial cartoons. Politicalcaricatures generally are thought provoking and strive toeducate the viewer about a current issue. A typical news-paper article has a great many words to deliver infor-mation and ideas whereas a political caricature can ad-dress issues to ground level and expose the reader tosharp ridicule by reducing an entire article down to sim-ple pictures. Political caricatures have been proved as apowerful vehicle for swaying public opinion and criticiz-

ing or praising political figures [3].

2.2 Caricature in Internet and Mobile Application

With the emergence of Internet and mobile technology, acaricature has been used for social communication andentertainment over web or mobile phone. The user canprotect their identity and real image from other users forsecurity purposes but still allows the basic facial gesturesto be recognizable. Caricature is very applicable to beused in network environment because of their simplifica-tion in representing a person, which can reduce the com-putational burden and the bandwidth requirement, com-pared to the real photo image. Caricature is used as a dis-play picture to create a memorable impression of a par-ticular user in visual messenger, visual chat room, bulle-tin board and forum. It is also used as an avatar in virtualcommunity such as virtual museum, virtual classroom, ininteractive movie and multiple user role-playing commu-nity in virtual game. Avatar is a graphical representationof a person in virtual environment. According to K. L.Nowak and C. Rauh [4], the people preferred an avatarthat is matched to their own gender, type (human) andwith other characteristics that are similar to their own.Thus, use of facial caricature of the people as an avatarmight be a good preference since the caricature can pro-vide users with valuable information about their partnersand can reduce uncertainty in their interaction. Caricatureis also used as an avatar in a blog, website, profile pageon the social networking and dating sites. Other than that,caricature also can be used in e-business card, e-greetingcard, e-logo or as a perfect gift idea for many occasions.Many research have been carried out concerning the use

of caricature in internet and mobile phone application [5],[6], [7], [8], [9]. J. Liu et al. [5] and R. Q. Zhou and J. X. Liu[7] changed the peoples real photograph captured bymobile camera to black and white caricature and then itwas used to synthesize the multimedia animation mes-sage and transferred it to the mobile terminal as an enter-tainment. M. Lyons et al. [8] used caricature of human

face in creation personalized avatar system. J. Liu et al. [6]proposed a prototype system for online chatting in whichcaricature was used for people to express their emotion orto show their identity to the others they communicatingwith M. Sato et al.[9] used caricature with a small set ofcharacteristic points for non-verbal communication inmobile phone.

2.3 Caricature in Face Recognition

Use of caricature for face recognition and perception hasbeen extensively studied in cognitive psychology, visualperception, computer vision, and pattern recognition ar-ea. Caricature is often used in facial recognition especiallyto recognize the criminal faces in police investigation orto identify subjects in forensic process. The victims orwitnesses are often asked to describe the facial features ofthe criminal and then, an artist will draw the caricatureaccording to the description. Caricatures are generallyperceived as better likenesses of a person than the veridi-cal images [10]. It is because the exaggeration of the dis-tinctive feature in caricature emphasizes the features ofthe face and enhances the recognition. The distinctivefeatures are important in face recognition and faces withunusual features are better recognized than more typicalfaces [11]. Caricature is not only used during recognizedfaces but also used to increase the amount of memorableinformation of the face encoded for later recognition.People often recognize faces from their own races betterthan those from other races [12]. J. Rodriguez et al. [12]used caricature to reduce other-race effect in facial recog-nition and to help individuals focus their attention to thefeatures that are useful for recognition. G. Rhodes et al.[13] found that line drawing caricatures can be better rec-ognized than the veridical image but photographic carica-ture did not. C. Frowd et al. [10, 14], and J. Smitaveja et al.[15] employed caricature in face recognition system usingdifferent approaches.

2.4 Caricature in Information Visualization andEducation

Basic concept of caricature that exaggerated the character-istic features of the subject and simplifies the overallstructure is adopted in information visualization for bet-ter understanding about the subject. It provides a power-ful metaphor for illustrative visualization. It is called ascaricaturistic visualization [16]. Caricature is also used tovisualize the differentiation of subjects or concepts thathard to be noticed or to ease the user in understandingthe difference of subjects. The potential applications aresuch as quality control, comparative biology, case-basededucation for medical students and deformation surveil-lance. Caricature is also used to enhance learning espe-


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cially for the complex learning. It helps the cognitive sys-tem to pick up distinctive features of the learned materialand minimizes any perceptual or representational confu-sion [17].

2.5 Caricature in User Interface AgentCaricature is also used as an interface agent to assist auser in accomplishing daily computer tasks such as sort-ing email, filtering information and scheduling meetings.It can help the user interact with the agent and under-stand the agents behavior or characteristic through theirexternal trait [18]. Caricature could also make a computermore human-like, engaging, entertaining, approachable,and understandable to the user, establish relationshipswith users, and make the users feel more comfortablewith computers [19]. It also creates strong implications inuser interaction with the system, which makes the usermore likely to be cooperative.

3 THEORIES AND RULES IN THE ART OF DRAWINGCARICATURE

3.1 Elements of Caricature

There are three essential elements that must be presentedin caricature [20],[21]. The first element is Likeness. Allgood caricature must have a good likeness of their sub-

jects. If the caricature cannot represent who it is supposedto be, then it is not a caricature. The second element isexaggeration. All caricature must have some form of ex-aggeration or a departure from the exact representation ofthe subject features. If there is no exaggeration, what wehave is a portrait but not a caricature. The degree of exag-geration employed by each individual caricaturist varies

enormously. Exaggeration is the essence of a caricatureand not a distortion. Exaggeration is the overemphasis oftruth, while distortion is a complete denial of truth [22]for instance if the mouth of a person is prominent, make itenormous and not make it tiny. However, there are ex-traordinary creative caricaturists who succeed in dis-torting some part of facial feature without losing theirlikeness. The last element is statement or subjective im-pression. Caricature must have something to say aboutthe subject. It might be something to do with the situationthe subject is drawn in, their personality through expres-sion or body language or visual fun of some aspect ortheir image.

3.2 Process of drawing caricatureDrawing a caricature is an inborn talent exits only in afew peoples [23]. This inborn talent embedded in theirsubconscious mind and hard to be explained. However,these talented people still have to be taught becausesometimes this talent may go unnoticed or undiscovereduntil later of life. Drawing caricature needs eye-hand co-ordination, which uses the eyes to pay attention to thesubject and the hands to draw the subject based on theinformation received through the eyes. In addition, weneed our brain to find the most prominent facial featuresthat make the person different from everyone else [21].The process of drawing caricature involves two basic

steps, observation and exaggeration.Observation is the most important things to do in

drawing caricature. The likeness can be lost very easily ifthe observation is not carefully accomplished. Caricaturistneed to understand what they see in the individual facethat makes him recognizable, observe which of the facialfeatures are larger, smaller, sharper, and rounder than

most peoples, identify the salient facial features thatmake him unique and extract his personal interpretation.Persons distinguishing characteristic can be determinedby comparing his/ her facial features with the reference.According to the psychologist [13], human beings have amean face recorded in their brain, which is an averageof faces they encounter in life. This mean face acts as areference. The distinguishable facial feature is the featurethat larger or smaller or sharper or rounder than average.However, a caricaturist considers the difference not onlyfrom the average face but also their surrounding featuresof the same subject. L. Redman [22] employed the princi-ple of relativity to define the distinctive facial features.

Relativity in the arts can be thought of as having twoparts: the relationship of things to others of their ownkind and the relationship of things to their surroundingand abutting elements. The first part gives us the infor-mation about the size or shape of the facial features com-pare to others such as the eye is larger than others where-as, the second part tells about the distance or aspect ratiobetween the facial features such as distance between eyesor ratio of the mouth width to the nose width. L. Redman[22] used in-betweener as a frame of reference for deter-mining how to exaggerate the subjects features. He exag-gerates any features of the subject that is different fromin-betweener. T. Richmond [20] defined the relationship as

the distances between the five simple shapes, their sizerelative to one another, and angle they are at in relation-ship to the center axis of the face. The five simple shapesare left eye, right eye, nose, mouth, and face shape. Heused classical portrait proportion as a point of referenceto observe where the subject's face might differ and de-cide what relationships to change and how much tochange them. The width of an eye is used as the primaryframe of reference. It is difficult to observe those faces inwhich the unique features are not obvious. In order tomake observation easy, simplify the face into five basicshapes by eliminating the details.

The second step is exaggeration. The way of the

caricaturists draw and exaggerate a caricature is dependon their style. Most of the artists start drawing a carica-ture with the simplest form or outline of the subject afterthey have determined the unique features or the relation-ship to be exaggerated from their observation. The simpleshape is easier to draw, control and exaggerate to the de-sired shape than ones with a lot of complex element. Thecaricaturist always creates several little drawing of differ-ent exaggerated facial features and then picks the best one[1]. After that, the detail of facial features will be added tocreate the likeness. Which facial features are the most im-portant part and should be looked and drawn first is var-


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ies to different artist. K. Bjorndahl [1] , and Shafali [21]consider that the eyes is the most important feature on theface, whereas T. Richmond [20] believes that the faceshape is the most important part and then eye and nose.The exaggeration is the manipulation of the relationshipof the features to one another in term of their distance,size and angle and not the features themselves taken in-

dividually. Even the eyes is bigger than other peoples, itcannot be simply drawn in bigger size. The rest of the faceshould be taken into account. Every change to one featurewill affect to the others features or called as action andreaction. For example, if the eyes are moved farther apart,the nose will move closer to the eyes. If the chin ischanged to become bigger, the top of a head will besmaller. This technique will create higher impact exag-geration. Other than that, basic knowledge of drawing,anatomical knowledge and experience in drawing a hu-man portrait are very helpful in drawing caricature.

4 CATEGORY OF FACIAL CARICATUREGENERATOR

Facial caricature generator originally is intend to generateall type of caricature and not only human face caricature.However, most of the previous studies only focus onproducing or synthesizing facial caricature. It is becauseface is a unique feature of human being and a substantialfactor in providing a large part of human identity. Faceshave been the subjects of interest of numerous studiessuch as in image processing, computer vision, biometric,pattern recognition, cognitive psychology, visual percep-tion, computer graphic and animation. Basically, there aretwo main purposes of study dealing with faces: for face

recognition that involves analysis tasks and face recon-struction that consist of synthesis tasks [24], [25]. Synthe-sizing facial caricature can be classified under the facereconstruction studies but also involves many other tech-niques from another area of study. In fact, it is also intro-duced as a part of non-photorealistic rendering technolo-gy, which attempt to mimic any existing artistic stylewhether in image-space or object-space. Facial CaricatureGenerator can be divided into two main category basedon their input data set: human centered approach that useword descriptions as input data and image centered ap-proach that use photographic face images as input data[26].

4.1 Human Centered Approach

A facial caricature is generated based on the users verbaldescription of facial features and the impressions aboutthe target face. This approach attempts to imitate how thesuspects face is drawn based on verbal description givenby the witness in police investigation. There have beensome studies on generating facial caricature from the us-ers linguistic description [27], [28], [29], [30]. These stud-ies are using fuzzy theory to express the meaning of thelinguistic term or word and to represent the parametervalue of each facial feature. These parameter values are

related to the linguistic terms. The increase and the de-crease of the parameter value are defined according to theLinguistic term. Linguistic terms used to describe the fa-cial features are such as big, small, round, thinand thick. Impression terms are such as pretty, se-vere, honesty, active[28], cheerful, quiet, ma-ture, youthful [30]. The general procedure for generat-

ing facial caricature from word description is shown inFig 1. The user will input words that describe their de-sired facial caricature image. The words have been de-fined in the fuzzy set. Some calculation will be done ac-cording to the inputted term to get the features parametervalues. The correspondence image in the database will belocated [30] or the average face that is used as an initialface will be transformed to the desired facial caricatureaccording to the parameter value [27], [29].

Fig 1 General procedure to generate facial caricature from word descrip-

tion.

Finally, facial caricature with obtained parameter valuesis presented to the user. If the output is not fit to the de-sired facial caricature image, some modification can bedone also using the linguistic term. S. Iwashita et al. [27],S. Iwashita et al. [29], and T. Onisawa and Y. Hirasawa[30] focused on how to seek the desired image of the faceso that the output really reflect the face image in the us-ers mind while J. Nishino et al. [28] concentrated on thelinguistic acquisition and emphasizing on the structure ofthe fuzzy set. The problems that always encountered inthese studies are the ambiguity meaning of the linguisticterm, the display of the facial features is not really similar

Input

output

caricature

Linguistic terms / words

- round eyes, big nose, little,more, very, slightly, etc

Lin uistic terms

Facial features arameter values

Relationship

Get the corresponding image accord-

ing to the parameter values

Modification


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to the defined term, the output are seem unnatural, lim-ited number of the defined linguistic term causes the de-sired facial caricature cannot be displayed and the mean-ing of the linguistic words do not express the size of thefacial features relatively to the size of the face.4.2 Image Centered Approach

This approach uses a 2D face image as an input data to

generate a facial caricature drawing. It attempts to imitatehow the caricaturists draw the caricature by looking atthe face image or at the real person or just based on theirmemory. This approach takes advantages of the imageprocessing technology to process input face images andother related analysis tasks. The first caricature generatorwas created by S. E. Brennan [31]. She extensively studiedthe style, theory and method of caricature generation andexamined the perceptual phenomena regarding to thedistinctive facial features and came out with some heuris-tic of the caricature generation process. The process ofcaricature generation was carried out on the basis of ex-aggerating the differences between the subject and the

average one. However, after that there have been manyother works with different approaches. Generally, theprocess of generating facial caricature can be described asshown in Fig 2. First, extract facial feature points from theoriginal face image. Then, find the distinctive featuresand exaggerate it to the new ones. Lastly, warp the faceshape of the original face image or the face sketch to thetarget position according to the new points in order toproduce a photographic caricature or sketch caricature,respectively. Some other works just connect the exagger-ated points using line or curve function to generate a faci-al caricature. This process involves many areas of study.Face processing and extraction is a subject research in

image processing and computer vision whereas linedrawing, stroke, image warping or deforming is in com-puter graphics and animation. To find the distinctive faci-al features and exaggeration rate need for knowledge ofcognitive psychology and visual perception.

5 PROCESS OF GENERATING CARICATURE FROMINPUT FACE IMAGE

The detail of the common process in generating facialcaricature is explained as followed.

5.1 Face Extraction

Facial feature extraction can be performed manually orautomatically. This step can take advantages of imageprocessing technology to process the face image such aspreprocessing, detection, localization, segmentation andfeature extraction. P. Y. Chiang et al. [32] utilized Cannyedge detector and Hough transform for iris localizationand applied Active Contour Model (ACM) to obtain thefinal face mesh. ACM only performs when the shape ofobject is simple, the background is monotonous and noprior knowledge [33].

Fig 2 General procedure to generate facial caricature from input face image

H. Koshimizu [34] also extracted iris using Hough trans-form and extracted other features using K-L expansion.Most of the existing works in generating caricature usedmodified Active Shape Model (ASM) [35], [36], [37], [38]and modified Active Apperance Model (AAM) [39], [40],[41], [42] to extract facial feature points. ASM and AAM is amodel of the shape variations and texture variations of facial

images using Principal component analysis (PCA). The coeffi-

cients of eigenshape or eigenface are used as parameters to rep-

resent the face shape and face texture. By adjusting the model

parameters, the model can be well fit to a new facial image and

can be used to automatically extract the facial shape feature

points [38]. The difference between ASM and AAM is the

ASM model uses local image textures in small regions oflandmark points, whereas the AAM model uses thewhole appearance[37]. AAM generally performs betterthan ASM [33]. Normally, automatic extraction of facialfeature points is done during run time whereas manualextraction is employed during learning or training phaseto get an average face or to get underlying features.5.2 Facial feature point definition

These defined facial feature points are also known as

landmark points. Other than that, we need to define whatparameters to be extracted. The parameters that are al-ways being used in caricature generation are facial fea-ture points, size feature, shape feature, aspect ratio, dis-tance between features and curvature property of eachfacial segment. The number of facial feature points usedby the existing researchers varies according to the pur-pose of their use but usually ranges from 50 to 300. High-er number of facial feature points is commonly used foranimation [32] and lower number is always used for mo-bile application [9]. High number of facial feature pointscan generate variety form of exaggeration without de-

input

face image

Face processing/ feature extraction

Extracted feature points

Find distinctive features

Exaggerate the features

New points / exaggerated features points

Transformation-

morph/ deform

Face

sketch

Output

Caricature

Other

sources


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stroy the likeness of the subject and more interesting butit has high computation and storage complexity whereasthe lower number of facial feature points can save thecomputation and storage cost but the result is less inter-esting and limited to certain exaggeration. Table 1 belowshows the landmark points used in some research works.

Table 1

Number of Landmark Points Used in Some Research WorksBasicfeatures

Research works

[43] [44] [45] [46] [32] [23] [9]

eyes 16 16 16 40 44 8 12

eyebrows 20 20 20 14 16 8 16

nose 12 12 12 25 22 5 3

mouth 17 20 20 24 18 6 10

ears - - - 12 - 10 10

Facecontour

19 19 24 25 19 9 9

Hair style - - - 31 - - -

Totalpoints

84 87 92 171 119 46 60

5.3 Distinctive Facial Features and Exaggeration

After the entire required facial feature points have beenextracted from the input face image, the distinctive facialfeatures need to be determined and then those featureswould be exaggerated to the desired form. According tothe theory and rule of drawing caricature, an input faceimage need to be compared with the reference face inorder to determine the distinctive facial features. The ref-

erence face can be either an average face or a standardface model. Average face or mean face is derived from thecollection of face images in database. Facial features arerepresented by using N points of x and y coordinates. Letsay, there are M face images in the database, the meanface can be calculated by

Ni

yM

yxM

xM

j

M

j

P

i

s

i

P

i

s

ijj

,...,2,1

1,

1

1 1

)()()()(

(1)

where xi(Pj) and yi(Pj) are the x and y coordinates for the i-

th feature point of the j-th normalized face data.Standard face model is a standard proportion of

the ideal face. V. Boyer [47] proposed a caricature model-

ing and rendering based on the Canon model as shown inFig 3. The exaggeration is applied on the differences fromthe face image and the canon model. B. Gooch [48] usednorm face as a reference to find the distinctive features tobe exaggerated. Four vertical lines are set to be equidis-tant while the horizontal lines are assigned distance val-ues 4/9, 6/9 and 7/9 from the top of the frame as shownin Fig 4. Furthermore, F. Ni et al. [49] proposed Self Ref-erence Model (SRM) to be used in caricature generationsystem. The distinctive features are properly estimatedand quantified by evaluating some differences betweenthe input face and the standard facial parameters accord-

ing to the SRM.

Fig 3Canon model [47]

Fig 4 Norm face grid

There are many other approaches that employed bythe existing works to find the distinctive facial features

and to assign the exaggeration rate, which will be ex-plained later in detail at section 6. The summarization ofthese approaches is shown in Table 2 below. There arethree approaches to find the distinctive features: interac-tive, regularity-based, and learning-based and three ap-proaches to assign or to define the exaggeration rate: in-teractive, empirical, and learning-based. However, thereis one approach that did not use this step in producingcaricature. It is predefined database of caricature illustra-tion.

Table 2

Different approaches in generating caricature

To Definethe Exagger-ation Rate

To Determine the Distinctive Facial Features

interactive regularity-basedlearning-based

interactive [53],[60][26], [31], [34], [38],[39], [41], [48],[55],[56],[57],[58],[61],[62],[63]

empirical[64], [32],[ 44], [54],[59]

learning-based [23], [65][43], [35][36]

5.4 Image Transformation

This is the last step in generating caricature. The most

common technique in computer graphics that can be usedto transform the input facial image to the desired facialcaricature is image metamorphosis or morphing. Imagemetamorphosis is implemented by combining imagewarping with color interpolation (cross-dissolve). Thereare two basic techniques of morphing: field morphingand mesh morphing [50]. The field morphing algorithmuses a set of control line segments to relate features in thesource image to features in the target image, while themesh morphing algorithm uses a rectangular grid ofpoints, which has aligned to the key feature location ofthe image. The field morphing method is easier to be ap-


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plied than the mesh morphing method but it takes moretime to compute [50], [51]. The most common problem inmorphing is to specify corresponding feature points ofthe images that can provide reasonable result and reducecomputational complexity [52] and to morph only specificparts of the face image while holding other parts constant[50]. The metamorphosis techniques that implemented by

the previous works in generating caricature are meshmorphing [23], [35], [36], feature-based morphing [32],[53] or known as field morphing, Thin plate spline warp-ing [43], and Delaunay Triangulation warping [54]. Fur-thermore, Arruda et al. [42] used deformable mesh tem-plate, M. Obaid et al. [40] employed quadratic defor-mation model and E. Akleman et al. [53] used implicitfree form deformation. C. C. Tseng et al. [44], B. Gooch etal. [48] and Z. Mo et al. [55] did not state the specific tech-nique of morphing.

The final caricatures produced by the existingworks come in a variety of styles such as photographiccaricature [23], [35], [36], [54], [55], [56] as shown in Fig

5(a), sketch caricature [43], [44], [55] in Fig 5(b), black-white illustration caricature [41], [48] and hand-drawn-like caricature as shown in Fig 5(c) [32]. Some of otherworks generate outline caricatures as shown in Fig 5(d)by connecting the exaggerated facial feature points usinglines [31],[34], [57], [58], [59], piecewise cubic Hermiteinterpolation [37], Bezier curves [39], and B-Spline curves[9], [38],[46].

(a) (b)

(c) (d)

Fig 5 (a) Photographic caricature [55] (b). sketch caricature [44] (c) hand-

drawn like caricature [32] (d) outline caricature [34]

6 DIFFERENT APPROACHES IN GENERATINGFACIAL CARICATURE FROM INPUT FACE IMAGE

6.1 Interactive Approach

In this approach, the distinctive facial features and theexaggeration rate are determined interactively by the us-er. The user can drag the points of the facial features toenlarge or reduce the size in order to generate the desiredcaricature. E. Akleman [60] came up with a very simple

algorithm, which utilize the interactive morphing tool togenerate caricatures as shown in Fig 6. He starts with anextremely simple template, which represents the essentialfeatures by a few numbers of lines. Only one feature isexaggerated at a time. He uses try and error method tofind the distinctive feature to be exaggerated. If that ex-aggeration did not create a likeness, go in the opposite

direction. If this too did not give the required result, re-turn the feature to its original position. The user mustcorrectly identify the distinctive features. It is difficult todo for the ordinary user and take a long time in doing tryand error process to get the desired result. The user is alsomight exposed to the risk of producing unrecognizablecaricature. E. Akleman et al. [53] further came up with anew deformation technique that uses simplical complexto generate caricature. He uses simplices (point and trian-gle) as a basic deformation primitive. Triangles can pro-vide shear transformation, which cannot be supported byline. The deformations are defined by a set of simplexpair. Blending function is used to interpolate the transla-

tion vector, which define the source to target mapping.This system can intuitively and interactively produce anextreme caricature but requires experience to meaningful-ly specify source and target simplices. All these worksrequire expert knowledge or skilled user input, whichlimits their applicability for every-day use [48].

(a)

(b)

Fig 6 (a) original face image and its simple template [53] (b) caricature

image and its simple template [53]

6.2 Regularity-based Approach

Regularity based approach is summarizing the rules ofcreating caricature and simulated by computer to gener-ate caricature automatically or semi-automatically. Thebasic rule in drawing caricature is exaggerating the dif-ference from the reference face. Average face is widelyagreed among the caricaturists and the researchers to beused as a reference face. S. E. Brennan [31] used an aver-age face derived from a collection of face images in data-base as a reference to find distinctive features. The posi-tions of 165 feature points on the input face image corre-sponding to the reference points in the average face aremarked manually with mouse click. These points are then


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caricaturist. C. C. Tseng [44], S. J. Gibson[54], and X.Guangzhe [59] determined the exaggeration rate empiri-cally by eigenvector and by a simple linear model [32].

Table 3

Regularity-based approach

Reference

face

Relativity

Relationship to othersRelationship tothe surrounding

Averageface

[26], [31], [34], [38],[39], [41], [55],[56],[57],[58],[61],[62],[63]

[44], [39]

Standardface model

[47],[48],[49]

6.3 Learning-based Approach

This approach is always used to capture the artist style orto simulate the caricaturist creativity that are difficult tocodify algorithmically such as the style of the particular

artist exaggerate the facial features, the style of stroke andthe style of sketch. The artists product will be learnedusing machine learning technique or artificial intelligentmethods. This approach requires extensive training data-bases containing pairs of original face image and its cor-responding caricatured face image drawn by caricaturist.L. Liang et al.[43] proposed the caricature generating sys-tem based on example. He analyses 92 face image-caricature pairs and classifies into 28 prototypes usingPartial Least Square (PLS). Each prototype contains sam-ples with similar direction of exaggeration correspond tosome facial features. Any input of face image would beclassified into one prototype. The facial features to be ex-

aggerated and the rate of exaggeration are estimated us-ing local linear model (least square). The exaggerationdirection and the selected facial features determined bythis method are limited and the distinctive facial featuresselected by the artist may cover different prototypes. J.Liu et al. [35] proposed a mapping learning approach togenerate facial caricature. He employs Principle Compo-nent Analysis (PCA) to obtain the principle component ofthe facial features and uses Support Vector Regression(SVR) to learn the mapping model in principle compo-nent space. A new input face photograph would be pro-

jected into the principle component space and the vectorthat represents the position of the target caricature couldbe predicted using the mapping model. This vector needsto be transformed from the principle component space tohigh-level feature space to get the caricatures shape. Bothof the works [35]and [43] used a linear methods to mapthe original face image to its corresponding facial carica-ture whereas J. Liu et al. [36] further came up with non-linear mapping model. He employs semi-supervisedmanifold regularization learning to reduce dimensionali-ty of the caricature and to build regressive model in orderto predict the caricature pattern from the given originalface image. K. H. Lai [23] and N. S. Rupesh [65] also be-lieve that generating facial caricature involves non-linear

exaggerations and not only linear exaggeration, whichscale the features with a factor as provided in most of theexisting caricature generation systems. They proposed aneural network based caricature generation. Feed for-ward back propagation [23] and cascade correlation [65]are adopted to capture the relationship between the dis-tinctive facial features and the changes from original face

image to caricature. The distinctive facial features are de-termined by comparing face image to the average face.This system cannot generate caricature exactly the sameas the artist drawing but still can be accepted as satisfac-tory result as shown in Fig 10. J. Liu et al.[35] and J. Liu etal. [36] learn the style of general artist by using handdrawn caricatures that are created by many artists overthe world while K. H. Lai et al.[23], L. Liang et al.[43] andN. S. Rupesh [65] learn an individual artist style and thecaricatures are drawn by a particular artist. K. H. Lai etal.[23], L. Liang et al.[43], and N. S. Rupesh [65] known assupervised learning whereas Liu et al.[35] and J. Liu et al.[36] as semi-supervised learning.

(a) (b) (c)

Fig 10: (a) original face image (b) hand-drawn caricature created by an

artist (c) the resulting caricature [23]

6.4 Predefined Database of Caricature Illustration

The distinctive features and exaggeration rate are not de-termined explicitly in this approach. Illustration carica-tures correspond to the original facial parts need to bedefined beforehand [46], [66], [67]. This approach is alsoknown as component-based [68]. An input face imagewill be extracted and caricature illustration closest to theextraction result would be selected from a database. T. W.Pai and C. Y. Yang [46] concentrated on classifying anddrawing methodology in order to ease the matching pro-cess between an input face image and the pre-generatedcaricature in facial bank. The resulting caricature generat-ed by T. W. Pai and C. Y. Yang [46] shown in Fig 11. The

problem of this approach is to obtain all the required cari-cature illustration in advance. If the correct caricatureillustration is not available in the database, the facial partof an input face image might be replaced with unsuitableone and resulted in producing unrecognizable caricature.Sometime, even the individual parts of the input face im-age are matched flawlessly, the resulting caricature bearvery little resemble to the person [68].


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(a) (b) (c)Fig 11 (a) original face image (b) matched face contour (c) caricature image

[46]

7 EXPRESSIVE FACIAL CARICATUREInstead of generating facial caricature without consider-ing the expression of the face image as explained in allprevious sections, there are some effort to produce anexpressive facial caricature [27], [29], [40], [68], [69], [70].In A. J. Calder et al. [69], a neutral expression face imageacts as a reference face. The expressive caricature is pro-duced by exaggerating the difference between an expres-

sive face image and the neutral expression face image. W.H. Liao and Chien An Lai[68] used facial component ac-tion definition (FCAD) to control a subset of facial param-eter to produce a multiple expressive caricature. M. Obaidet al. [40] defined the expression using facial action cod-ing system (FACS) and used quadratic deformation mod-el to synthesize an expressive caricature. A. J. Calder et al.[69] generated an expressive caricature from an expres-sion face image whereas [40], [68], [69] produced multi-ple expressive caricature from a neutral expression faceimage. The multiple expressive caricatures produced by[40] shown in Figure 12. However, the expressive carica-ture produced by them [40], [68], has less or no exaggera-

tion if compared to the original expressive face image. S.Iwashita [27] and J. Nishino [29]generate an expression-less facial caricature first and then, the facial features arechanged to the desired facial expression based on the fa-cial expression definition obtained from questionnaire,which contains a parameter value of each facial featuresfor various types of facial expression. The process of pro-ducing an expressive caricature is generally similar to thegeneral process of generating caricature that explainedbefore in section 5 except they need to add some methodto control the location of facial feature points according tothe type of facial expression. Most of them assumed that aparticular expression of all people has the same expres-

sive face and used the same value of parameter to changethe facial feature points.

(a) (b)

(c) (d)

Fig 12 (a) input face image (b) natural expression (c) smile expression (d)

surprise expression [40]

8 ISSUES AND FUTURE RESEARCH8.1 Human Centered Approach

All the previous studies assume that the linguistic termhave the same meaning for all people but according to H.Benhidour and T. Onisawa [26], the same faces can bedifferently interpreted by different people. So, the mean-ing of the linguistic term for drawing facial caricature

should be represented according to the type of users andmany other factors. Instead of using fuzzy theory in rep-resenting the meaning of the language terms, other suita-ble approaches or hybrid approach might be investigated.Other than that, linguistic term in those studies is in-putted in text format. Thus, use of voice as an input togenerate facial caricature can be an interesting subject tobe studied.

8.2 Reference FaceThe distinctive facial features can be determined by com-paring one face with the reference face. Average face iswell accepted as a reference face for the caricature gener-ated system which is used by the skilled user or the artis-

tic effect of the resulting caricature is not considered, butif the system attempt to imitate the style of a particularartist, it begs the questions: Does the average face ob-tained from a set of face images in the database equal tothe mean face in the mind of the artist? and Do a setof face images in the database represent all the faces thatthe artist encounter in life?. If the average face does notrepresent the mean face in a particular artists mind, itmight affect the generated caricature because the differentaverage face will produce different caricature [57] andhow a person interprets the human face is different fromothers [26]. Therefore, to find a method and the best ref-erence face that exactly represents the mean face in art-

ists mind can be one of the future works.

8.3. Correspondence of Feature Points

In the caricature generation process, comparison of facialfeature points between two different face images need tobe implemented such as between the input face imageand the hand-drawn caricature image in learning-basedapproach or between the input face image and the refer-ence image in finding the distinctive facial features. Mostof the previous works manually labeled the position offacial feature points in one face image corresponding to


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the facial feature points in another face image. They usedtheir experience and judgment to select the position of thefacial feature points, which is prone to human mistake,and take a lot of time. The questions here are how to findthe corresponding feature locations in different faces?Does the location of facial feature points in one face isaccurately correspond to the facial feature points in an-

other face?

8.4. Learning-based Approach

The crucial problems in this approach are limited datacollection of face image-caricature pairs and inconsistencyof the artist styles. To find a method, which can generatecaricature that exactly the same or very close to the art-ists work under that problem is one of the future chal-lenges. This approach still need to be explored since thenumber of the previous works using this approach is stillvery reduced. Furthermore, none of them extracted therule of artists style, explained how the artist exaggeratedthe facial features and showed the comparison of the dif-ferent artists style. If the rules of the artists style can besummarized in formula or words, it can enhance the artis-tic effect of the current facial caricature generator and thestyle of a particular artist can be preserved even when theartist and his works are no longer available. In addition, ifthe art-style of the caricaturist can be understood, it cangive many beneficial to the application of the caricature[25] . A good facial caricature generator system not onlyable to simulate the artist works and produce an interest-ing caricature but also at the same time allows users toeasily interact with the system. Thus, how to integratebetween learning-based approach and interactive controlof the exaggeration rate also can be one of the futureworks. The user might be allowed to produce caricaturein their style of exaggeration based on the style of severalartists.

8.5. Pose of the Input Face Image

In previous works, pose of the input face image and thegenerated facial caricature are limited to the front-viewwhereas in the real world, most of the caricature drawnby the artist is in multi-view according to the prominentfeatures of the person and the pose of original face imageis also not only in front-view. In fact, sometime the artistuses more than one original face image with various pose

to get more information about the subject in order todraw very good caricature. Therefore, multi-view facialcaricature generation system needs to be developed infuture.

8.6. Elements of Good Caricature

There are three elements of good caricature: likeness, ex-aggeration and statement or subjective impression. Thefirst two elements are successful displayed in current car-icature generation system but how to express the thirdelement in the facial caricature generator can be an inter-

esting subject research.

8.7. Non-photorealistic Rendering Techniques

This technique such as pen-and-ink, brush, hatch, paint-ing etc need to be improved and enhanced to render thefacial caricature texture and the style of artist drawingneed to be captured and combine with the existing non-photorealistic rendering technique in order to let the cari-cature approaching the artists product.

9 CONCLUSION

This paper attempts to provide a comprehensive surveyof application, theory and research on facial caricaturegenerator and to provide some structural categories forthe approach and method described in the previous pa-pers. The use of caricature can be seen in various applica-tions such as in entertainment and political magazines, inInternet and mobile application, in face recognition, ininformation visualization and education, and in user in-

terface agent. The important things in process of drawingcaricature are observation and exaggeration. The chal-lenge of the artist is to find the most distinctive facial fea-tures that make the person different from everyone else,to exaggerate that features and at the same time to main-tain the likeness of the person. Facial caricature generatorcan be categorized into two according to the input dataset, image processing approach and human centered ap-proach. The first one uses face image as an input data andanother one uses word description as an input. The firstcategory is studied by the researcher and discussed in thispaper more than the second one. The approaches of gen-erating facial caricature from the input face image can be

divided based on how the distinctive facial features aredetermined and how the exaggeration rate is defined.There are three approaches according to how the distinc-tive facial features are determined: interactive, regularity-based, and learning-based and there are also three ap-proaches based on how the exaggeration rate is defined:interactive, empirical and learning-based. However, thereis one approach that did not define the distinctive facialfeatures and the exaggeration rate. It is predefined data-base of caricature illustration. Regularity-based approachis the most widely used approach to determine the dis-tinctive features in the previous work, whereas most ofthe previous works used interactive approach in definingthe exaggeration rate. Nevertheless, the learning-basedapproach seem to become a significant approach due tothe researchers do not only intend to produce a simplecaricature but also consider the artistic effect of caricatureand endeavor to capture the style of the artist in theirworks. In addition, there is a lack of integrity in learning-based approach and interactive approach in order to de-velop a good facial caricature generator system. Facialcaricature generator also has been enhanced to generatemultiple expressive facial caricatures which can be em-ployed in caricature animation and other application inthe future. Nonetheless, to generate a facial caricature that


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exactly similar to the artists product still remain as anopen problem for further investigation and the existingissues in the previous works could spur researchers toexplore and enhance this field in future.

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Suriati bte Sadimon received her BSc in Science, Computer with Educa-

tion from Universiti Teknologi Malaysia and her MSc in Distributed Mul-

timedia System from University of Leeds, United Kingdom. She is current-

ly persuing her Phd study at Universiti Teknologi Malaysia. Her area of

interest includes computer graphics, face image processing and machine

learning.

Mohd Shahrizal Sunar received his BSc in Computer Science from Uni-

versiti Teknologi Malaysia, his Msc in Computer Graphics and Virtual

Environment from University of Hull, United Kingdom and his Phd in

Computer Graphics from Universiti Kebangsaan Malaysia. Currently, he is

a senior lecturer and a head of department of Computer Graphics and Mul-

timedia, Universiti Teknologi Malaysia. His area of interest includes com-

puter graphic, virtual reality, game programming and image processing.

Habibollah Haron received his BSc in Computer Science from Universiti

Teknologi Malaysia, his Msc in Computer Technology in Manufacture from

Universiti of Sussex, United Kingdom and his Phd in Computer Aided

Geometric Design from Universiti Teknologi Malaysia. Currently, he is an

Associate Professor and a head of department of Modelling and Industrial

Computing, Universiti Teknologi Malaysia. His area of interest includes

computer aided geometric design and soft computing.

Documents

A review of Facial Caricature Generator