Example-Based Automatic Caricature Generation
Wei Yang

Kouki Tajima

Jiayi Xu

Masahiro Toyoura

Xiaoyang Mao

University of
Yamanashi

University of
Yamanashi

Hangzhou Dianzi
University

University of
Yamanashi

University of
Yamanashi

Abstract―Caricature is a popular artistic media widely used
for effective communications. The fascination of caricature lies
in its expressive depiction of a person’s prominent features,
which is usually realized through the so called exaggeration
technique. This paper proposes a new example based automatic
caricature generation system supporting the exaggeration ofvisual appearance features. The system comprises theconstruction of a learning database and the generation of
caricatures. The construction of the learning database links thepairs of facial images and corresponding caricatures. Given an
input face, the system automatically compute the feature vectors
of facial parts and hairstyle, and search the learning databasefor the exaggerated parts by using the most prominent features.
Experimental results show that our system can achieve thecontrol over the degree of exaggeration and the exaggerated
results can better represent the features of the subjects.
Keywords- caricature; example-based; exaggeration; visualappearance facial feature;

I. INTRODUCTION
Caricatures serve as effective media for communicationin many settings. These “deformed” images, whichemphasize a person’s most prominent features to make it easy
for observers to identify the subject at a glance, range fromartistic portraits to satirical or parodic illustrations andcomics. People now use them as everything from gifts toresources for criminal investigations and satires of politicians.
Caricatures are a highly individualized form of art, but not
everyone is capable of drawing a caricature on his or her own.
Given the challenges involved, people who want caricatures
for personal use would benefit greatly from a generation
system that anyone could use to produce caricatures without
much difficulty. This paper proposes a new automatic
caricature generation technique: an example-based caricature
generation system that uses exaggerations of visualappearance features.
Since the 1980’s, there are tremendous of research work
have been conducted on the computer generation of
caricature [1-2]. Most of early works, however, rely more orless on user’s input for either extracting features from input
face or controlling the style of resulting caricature. Recently,
example based approaches [15, 16, 17, 18, 20] are attracting
large attentions for its advantage of being able to reflect theunique style of individual artists. Those methods rely on
training data comprising many sets of input images and theircorresponding caricatures. They allow users to express
divergent artistic styles by simply substituting the appropriate
examples into the target product. To ensure successful

learning, feature vector design becomes very important. Mostof existing example based methods use eigenspaces defined
via Primary Component Analysis (PCA) of face features.
Since people normally concentrate on visual appearance
features, such as the shape of individual parts (eyes, nose, andmouth, etc.) as well as the composite arrangement of thoseparts when they draw caricatures, methods that use
eigenspaces may fail to reflect artists’ styles fully. Our
technique takes the example-based approach but also
incorporates part-specific learning to enable the exaggerationof visual appearance features. Furthermore, new algorithms
have been implemented to automatically compute thefeatures of hairstyle which are not well addressed in theexisting systems.
The remainder of the paper is organized as follows: after
reviewing the related works in Section 2, Section 3 firstpresents the overall structure of proposed system and thendescribes the details of algorithms. Section 4 describes theresults of experiments and Section 5 concludes the paper.
II. RELATED RESEARCH
There is a considerable amount of past research on
computer generation of caricature. Generally, these existingstudies fall into three broad categories: interactive, rule-basedand example based.
Interactive methods give users the control over thefeatures to be exaggerated as well as the degree ofexaggeration, but on the other hand usually add more loads
to users [3]. Also it can be difficult to produce ideal results
for a user without the knowledge on caricature. Akleman etal. [4] provided a simple morphing template for the user tomanually deform the facial features. Later, they improved thealgorithm with a new deformation algorithm that uses
simplicial complex [5]. Gooch et al. [6] converted a
photograph to a simple line illustration, and then manipulated
the feature grid imposed on the illustration.
Rule-based approaches simulate the predefined rules todraw caricature. Most rule-based methods produce anexaggerated representation by analyzing the subject’s
features and then manipulating an average model accordingly.
The first such work by Brennan [7] puts 165 feature points
on the “average face”. The feature points are moved with anamount proportional to the difference from the correspondingreference points, and are connected to create a line-drawing
caricature. Koshimizu et al. [8] applied the same idea in theirinteractive system (PICASSO) which can generate very
impressive line drawing style caricatures. Chiang et al. [9]proposed a method by morphing a caricature drawn by theartist based on the difference from average model. Mo et al.
[10] used normalized deviation from the average model toexaggerate the distinctive features. Teseng et al. [11, 12] usedboth inter and intra correlations of size, shape and position
features for exaggeration. They subdued some of the features
to emphasize the other features. Chen et al. [13] considered
the two relative principles described in [14], and proposed“T-Shape” rule for emphasizing the relative position among
facial elements. They measured the similarity between the
caricature and the photograph with Modified Hausdorff
Distance and minimized the distance to improve their results.
Under a rule-based method, reflecting differences indrawing styles normally requires making changes toparameter extraction processes and creation rules for eachstyle. While example-based methods rely on training datacomprising many sets of input images and theircorresponding caricatures, they allow users to express
divergent artistic styles by simply substituting the appropriate
examples into the target product. The example-based method
proposed by Liang et al. [15] separates a portrait into twomodels—polyline-based
shapesandshading-based
textures—and learns from a collection of actual professional
portraits to refine and integrate both models, thereby
converting the input facial image into a caricature. Shet et al.
[16] used cascade correlation neural network to learn theexaggeration degree of caricaturist. Liu et al. [17] applied
PCA to obtain the principle components of the facial features,and then used Support Vector Regression (SVR) to predictthe result for given a face image. They further proposed a
non-linear mapping model using semi-supervised manifold
regularization learning [18].
In rule-based and example-based methods alike, feature
vector design plays an integral part in improving overall
caricature quality. Many existing caricature generation
techniques use eigenspaces defined via PCA. However, whenpeople draw caricatures, they normally concentrate on visualappearance features such as the shape of individual parts(eyes, nose, and mouth, etc.) as well as the composite
arrangement of those parts. In that sense, methods that use
eigenspaces may fail to reflect artistic styles fully. Our study
takes the example-based approach but incorporates partspecific learning to enable the exaggeration of visualappearance features.
III. PROPOSED METHOD
Figure 1 provides an overview of the proposed method,which comprises the construction of a learning database andthe generation of caricatures. The construction of the learning
database uses pairs of facial images and corresponding
caricatures as input. Based on geometrical shape information,
extracted from the facial image using the active shape model(ASM) for facial feature points detection [21], and hairregions, extracted using our original method, ourconstruction process calculates visual appearance feature
vectors and links them to the corresponding caricature parts.
Given that males and females have different facial features,
we created new ASM average models for males and females,

respectively, in order to improve ASM fitting accuracy.
Figure 2 shows the control points of the ASM model that weused in our system.

Figure 1. System framework

Figure 2. The control points of the ASM model
used in the proposed system

When generating caricatures, our system first calculates afeature vector for each part using the method same as thatused for the database construction step. Next, the system
performs exaggeration processing on the feature vectors anduses the exaggerated feature vectors to search the example
database for the exaggerated parts. Finally, the systemarranges the gathered caricature components to generate a
caricature.
A.
Designing and extrcting visual appearance feature
vectors
Humans perceive faces based more on characteristic
information—large, thin, and drooping, for example—thanon precise shape information. For our study, we sought
advice from professional caricaturists on the features of five
facial parts (eyebrows, eyes, nose, mouth, and hair) andincorporated that input into the design of our visualappearance feature vectors, which are shown in Figure 3. The
feature vectors for the different parts all have different

Figure 3. The feature vectors used in the proposed system

numbers of dimensions, with the coordinates for eachdimension normalized to the range of real numbers from 0.0
to 1.0The eyebrow features that have the strongest visual
impact are thickness and angle. As eyebrow thickness
normally tapers off gradually near the side of the head, oursystem measures vertical thickness at two locations: the innerend of the eyebrow and the outer end of the eyebrow.
Eyebrow angle, meanwhile, varies according to the change invertical position on the outer end relative to the vertical
position of the inner end. We thus select two points on theouter end and measure the angles of the straight lines
connecting the two selected points to the inner end. Eyes also
have two main defining traits: shape characteristics (thin, slit
eyes or large eyes, for instance) and angle characteristics(eyes that slant upward or eyes that droop downward, forexample). Shape features are defined by the aspect ratio of
the eye, while angle features are defined by the angle of thestraight line connecting the inner corner of the eye and theouter corner of the eye. Nose shape corresponds to the ratio
between nose width and nose height, while nose size depends
on the ratio of nose width to overall face width. The amountof space between the nose and mouth is another person-toperson variable; as shown in Figure 3, we also incorporate
two types of top-down angles into the features. For the mouth
feature, we use the ratio between mouth width and mouthheight to define shape and the ratio between mouth width andoverall face width to define size.
To determine the hair feature vector, one first needs toextract the hair region. However, the sheer person-to-person
diversity of hairstyles has prevented researchers fromdeveloping a hairstyle-extraction equivalent of the ASM
method for facial shape extraction. For our study, we
implemented our own original hair region extraction method.
First, we use a region growing algorithm to segment theimage into regions. The algorithm places multiple seeds on
the image, expands the seeds along gradients, and thendivides the image into regions according to the boundaries

that form where the gradients are high. The number of image
regions thus equals the number of seeds placed on the image.
Our system automatically sets seeds for three regions: theskin region, the hair region, and the “other” region. The
automatic setting process uses ASM control points andfollows the steps below.
Setting the skin region seedFirst, the system obtains the skin color from the ASM
control points located on the tip of the nose, cheeks, and otherpositions that best represent the subject’s skin color. The
system then looks at the colors of the points that have a highprobability of corresponding to skin, such as the areas justinside the tip of the nose, the cheeks, and the facial profile,and places skin region seed as appropriate.
Setting hair region seed
Based on the safe assumption that the hair region lies on
the upper portion of the subject’s head, the system uses theASM control points on the upper portion of the head to set
hair region seed. Forehead size varies from person to person,
however, so the control points do not always lie in the hairregion. In order to obtain points in the hair region only, thesystem compares the skin color obtained in the preceding stepwith the colors of the control points in question. If a control
point corresponds to skin color, the system gradually moves
the point until it reaches the hair portion.
Setting “other” region seedFirst, we assume that the person in the input image ispositioned near the center of the image and that a backgroundis visible behind the person. Based on this assumption, thesystem places “other” region seed near the top edge, left edge
and right edge of the image. The system does not place a seednear the bottom edge, as a subject’s hair might extend to thebottom of the image. However, a person’s hair is normallyparted by his or her face, neck, or chest. This means that the
middle portion of the bottom edge that intersects with the line
extending straight down from the center of the subject’s faceoften lies in an “other” region outside the hair region. The
system thus places seeds in this area, as well.

(a) Seeds setting(b) Segmentation(c) Hairstyle feature
results
results
vectorFigure 4. example of obtaining a hair region and its feature vector

Figure 4 illustrates an example of using the above
procedure to divide regions. Figure 4(a) shows the results of
the automatic seed setting results. There are several seeds inthe Figure. The light gray circles on the subject’s hair are thehair region seeds; the dark gray circles on the subject’s face
are the skin region seeds; and the black circles on the top, leftedge, and center-bottom area are seeds indicating “other”
regions. Figure 4(b) shows the results of segmentation using
the proposed algorithm. The brightly colored area at the topof the subject’s head is the area that the system determined tobe the hair region. As the Figure shows, the system
successfully obtained the general shape of the subject’s hair.
The system then uses the obtained hair region to calculate
the feature vector for capturing the visual appearance of thehairstyle. Generally, hairstyle types fall into basic lengthcategories: short, semi-short, semi-long, and long are several
examples. In other words, how far a person’s hair goes down
his or her head is one important feature of the person’s
hairstyle.
In addition to length, volume is another key element of
how a person’s hairstyle looks. To establish a feature vector
that expresses both length and volume, our method involves
drawing straight radial lines out from the center of the face at
certain angle intervals and using the distance separating thetwo intersections between each line and the hair region
boundary (see Figure 4(c)). As Figure 5 shows, the system
uses the proportion between Hi (the distance between the twointersections) and Fi (the distance from the interior
intersection to the center). Hi value of 0 means that there isno hair around the face, while an Hi value of greater than 0
indicates the volume of the hair. This makes it possible todeal with volume and length in a uniform fashion. The hairfeature vector created when straight lines are drawn at angle
intervals of 2 / has n dimensions.
B. Searching for similar parts
Using the feature vectors defined above, the system
calculates the distance between the parts of input image andthe parts of the examples in the learning database, and thensearches for similar parts. This requires comparisons offeature vectors that include dimensions with different
properties, such as length ratios and angles. For our method,
we thus normalize each feature vector on the feature axis inadvance.

Figure 5. Hairstyle feature vector

We use the Euclidian norm (L2) to calculate the distances
between vectors, taking the part with the lowest Euclidian
norm as the similar part. Formula (1) expresses face part
similarity, where the vectors for comparison are
,
,…,
and
,
,…,
, respectively.
(1)
As the presence or lack of hair has a significant impact ona person’s visual appearance, our system calculates similarity
based on both the Euclidean norm for the feature vector andan item that reflects the differences in hair presence in thecorresponding direction. Formula (2) expresses hairsimilarity for use in the search process.
2
0
0
Where
1
0
In Formula (2), r is a coefficient for adjusting the weightof differences in hair presence.
Using the formulas above, the system calculates thesimilarity between each feature vector obtained from the
input image and the corresponding feature vector from thedatabase and then uses the most similar item as a caricature
part.
C. Exaggerating feature vectors
In order to preserve the artistic style in which specificparts were drawn, our method achieves exaggerated
depictions by exaggerating the visual appearance feature
vectors of the various parts and using the exaggerated vectors
to obtain similar part caricatures from the example database.
As shown in Figure 6, for each part in the input image, oursystem calculates its difference from the average of thedatabase in the feature vector space. Finally, the system shifts
the feature vector in the direction of the difference vector inaccordance with the required exaggeration level and uses theresulting feature vector for search purposes.

D. Arrange the part images
After obtaining caricatures for all the parts from theexample database, the system places the caricatures
accordingly and generates output. The obtained ASM control
point information provides a basis for calculating thepositional relationships between parts in the input image. Our
method thus uses this control point information to arrange the
caricature parts in the output image.

Figure 6. Exaggerating feature vectors

Although it is logical to assume that a feature is more
prominent when there is a larger difference between thecorresponding feature vector dimension and the average, therelationship also depends on the distribution of coordinate
values in said dimension. Mo et al. [10] proposed a methodthat supplements the subject’s variations from the averageface with the distribution of his or her features themselves,
which thereby determines exaggeration quantities. Methods
that rely solely on differences from the average facesometimes fail to generate accurate caricatures because there
is no mechanism for assessing the prominence of a givenfeature difference relative to other feature differences.
Consider, for example, the horizontal spans of a person’s eyes
and mouth. Assume that the difference from the average forboth features is 2 cm. As mouth width generally varies more
broadly from person to person than eye width does, a 2-cm
variation in eye width would represent a more prominent
feature than a 2-cm variation in mouth width would.
Exaggerating the features with the largest deviations from thestandard distribution thus makes it possible to capture a
subject’s distinctive features more accurately. Our system
uses the distribution of example data in the database tonormalize the difference for each dimension, thereby making
it possible to calculate difference vectors that produce more
prominently exaggerated features.
The following formula determines the coordinate value
for each dimension, where V’ is the exaggerated feature
vector.
(3)
Where

,
,
,

,…,

,…,

: Feature vectors for the
input image,…,: Example data average: Example data standard
deviation

In Formula (3), k is a coefficient for determining theoverall exaggeration rate. Setting k to a positive value
exaggerates the subject’s features, while setting it to anegative value brings the subject’s features closer to theaverage.

(a) Obtaining the(b) Placing items
corresponding position fromin the obtained positions
the input image
Figure 7. Part image placement

The process of obtaining positional information fromASM control points is as follows. First, the system obtains
the bounding rectangle for the control points that constituteeach part. Next, the system determines where each part lieson the face by calculating the position of the bounding
rectangle for each part relative to the bounding rectangle forthe profile.
After obtaining positional information from the input
image, the system uses the profile caricature to obtain therectangle indicating the range of the face. Finally, the systemcompletes the caricature by placing the caricature images forthe various parts based on the positional information.
Figure 7 illustrates part placement. Figure 7(a) depicts theprocess of obtaining the bounding rectangles andcorresponding coordinates from the ASM control points in
the input image. The system generally uses the central
coordinates for each rectangle, but the nose is one exception:
the system uses the coordinates corresponding to the positionat the very bottom of the nose itself. Figure 7(b) illustrates
the process of placing caricature parts based on the obtained
position information. By matching the obtained coordinateswith the corresponding coordinates in the parts, the system
places the parts in the caricature.
IV.

EXPERIMENT

A. Results and discussions
We built a prototype system and test it with an example
data sets comprising pairs of input images and caricatures for
21 males and 10 females, provided by a company that makes
caricatures for commercial use. Figure 10 shows several
results generated with the prototype system. Figure 10(b1, c1,d1) are the caricature results for an input image of Prime
Minister Abe. We applied exaggerations to the caricature,creating the image in Figure 10(c1). One can see that the hairin the caricature has a bit more volume overall than the hairin the input image, but the hair in both images comes down
to the subject’s ears. The system successfully obtained thegeneral shape of the subject’s hair. Looking at the facial
features in the images, one can see that the characteristics ofthe eyebrows in the original picture—somewhat thick andslightly drooping—are evident in the caricature. Although thesystem selected eyes for the caricature that were slightly
bigger and more horizontally even than the eyes in the input
image, the differences are relatively minor. The nose also
exhibits some similarities and differences: the caricature doesa reasonable job of reflecting the gradual top-to-bottom
widening of the nose but is too wide overall. The nasolabial
folds (smile lines) also extend in different directions, but thisis because the system does not currently include a method for
obtaining facial wrinkles and folds. The mouth selected forthe caricature, meanwhile, is slightly larger than the mouth in
the input image. For comparative purposes, we created a
caricature with exaggerated features (Figure 10(d1)) and a
version with averaged (by setting k in Formula (3) to anegative value) features (Figure 10(b1)). The face in the input
image has narrower, slightly droopier eyes than the example
average, so the exaggerated depiction makes those featureseven more prominent. In the averaged image, on the other
hand, the eyes are slightly bigger and more horizontally even.
The exaggerated depiction also makes the subject’s mouthsmaller, while the averaged version makes it appear bigger.
Figure 10(b2, c2, d2) shows the caricature results for an
input image of Deputy Prime Minister Aso. Figure 10(d2)illustrates the results of the exaggeration process, which
produced changes in the appearance of the subject’s
eyebrows and nose. Although the subject’s eyebrows startedout basically level, the exaggerated version made them droop
slightly. The nose in Figure 10(d2) is a different part fromthose used in the other caricatures, but the shape is essentiallythe same. In the averaged version, the eyebrows, eyes, andnose are different. The Figure also shows that the eyebrowsgrew gradually more arched as they approached the averagemodel, while the eyes move from a slit shape to a larger, more
open shape. The closer the image gets to the average model,
the more gradual the top-down spread of the area under thenose becomes. These results suggest that the system could
produce an exaggerated representation of the Deputy Prime
Minister Aso image, as well, particularly with regard to thesubject’s eyes and eyebrows. As all the caricatures used as
examples for the present study were of smiling faces,
however, the system generated a smiling caricature for Prime
Minister Abe’s face even though the subject is not smiling in
the input image. Future research will thus need to constructdatabases that account for cases where a subject’s smile
affects features that are generally similar when the subject is
not smiling. In addition, the current implementation of thesystem does not take nasolabial folds (smile lines) into
consideration. However, given that the example data used in
the experiment included images containing nasolabial folds,
the end results produced some caricatures with nasolabial
folds that did not do a good job of capturing the subject’s
actual features. These lines are vital to conveying the

subject’s age and other characteristics, so we plan to improve
the implementation by treating nasolabial folds as individual,
independent parts.
B. Subject evaluation experiment
We conducted a subject experiment to validate theeffectiveness of the proposed method. First, we generated
three caricature images for each facial image (an averaged
caricature, a caricature with no exaggerations, and a
caricature with exaggerations). We then used the Thurstone
method of pair comparisons to determine which caricaturemost closely resembled the original image. We showed eachsubject a set of three visual stimuli, as shown in Figure 8. The
image in the middle was the original facial image, and theimages to the left and right were two caricature images
randomly selected from the set of three caricature images forthe corresponding facial image. We had the subjects choose
the caricature image (the image on the left or the image on
the right) that most closely resembled the image in the middle.
Totally three (3C2) sets of such images are presented tosubject for each input image.

Figure 8. A set presented to a subject

15 university students volunteered as the subjects. Eachof the 15 subjects provided answers on five input face images.
Thus the total number of test instances was 225 (15 x 5 x 3).
Table 1 shows the evaluation results in a pair comparison
format. When the subjects compared caricatures with noexaggerations and averaged caricatures, for example, 51
subjects deemed the caricatures with no exaggerations tostrike a stronger resemblance with the original image. The
figure in each Table cell thus represents the number of times
the subjects indicated that the caricature for that column moreclosely represented the original image than the caricature forthe corresponding row. Figure 9 is a bar scale showing theresults of a Thurston method-based analysis. The left side of
the scale represents a lower winning rate, while the right sideof the scale represents a higher winning rate. In addition todemonstrating the ranking of the different choices in a set, theThurstone method also uses the spacing between the various
choices to show relative differences in winning rate. As
shown in Figure 9, the highest winning rate (the highest
percentage of subjects deeming the images in that category to
be the closest representations of the source image) belonged
to the caricatures with exaggerations.

TABLE I.
Times
Averaged
caricature
Caricature
with noexaggerations
Caricaturewithexaggerations

EVALUATION EXPERIMENT RESULTS
Averaged
caricature

Caricature
with noexaggerations

Caricaturewithexaggerations

0

51

48

24

0

41

27

34

0

Figure 9. Results of Thurstone method-based analysis

V. CONCLUSION
The proposed method aims to facilitate the process ofreflecting an artist’s style by using the facial visual features
captured by the artist. Our experiment, which used a limited
example data set, showed that the method is capable ofgenerating exaggerations of parts that feature prominently insubject faces. Although the experiment did not use an
extensive collection of example data, companies engaged incollaborative research have already built stores of example
data images numbering into the tens of thousands. Moving
forward, we plan to gather more example data and conductsubjective evaluation experiments to design feature vectors
for dealing with the exaggeration of facial parts arrangement,
which is considered to be even more important than
individual facial parts.
VI. ACKNOWLEDGEMENTS
This research was supported by JSPS KAKENHI Grant
Number 25280037 and 26240015, the Natural Science
Foundation of Zhejiang Province, China (No. Q12F020007),and the Zhejiang Provincial Education Department, China(No. Y201121352).
[1]

[2]

[3]

KANEKO. M., Computer caricatures, Journal of the Institute of Image
Information and Television Engineers, vol. 62, no. 12, pp. 1938-1943,
2008.
SADIMON, S. B., S UNAR, M. S., M OHAMAD, D., AND HARON,
H. 2010. Computer generated caricature: A survey. In Proceedings ofInternational Conference on Cyber Worlds, IEEE, 383–390.
TOSHIAKI. N, TAKESHI. N, AND HIROSHI. H. 2009. Applying
Various Artists' Style of Exaggeration to a Facial Caricature Drawing
System with an Interactive Genetic Algorithm, The journal of theInstitute of Image Information and Television Engineers 63(9), 12411251.
[4] AKLEMAN, E. 1997. Making caricature with morphing. In ACM
SIGGRAPH 97 Visual Proceedings: The Art and Tnterdisciplinary
Programs of SIGGRAPH’97, ACM Press, ACM, 145.
[5] AKLEMAN, E., PALMER, J., AND LOGAN, R. 2000. Making
extreme caricatures with a new interactive 2D deformation techniquewith simplicial complexes. In Proceedings of The 3rd Internatonal
Conference on Visual Computing, 165–170.
[6] GOOCH, B., R EINHARD, E., AND GOOCH, A. 2004. Human facial
illustrations: creation and psychophysical evaluation. ACM
Transaction on Graphic 23, 1, 27–44.
[7] BRENNAN, S. 1985. Caricature generator: the dynamic exaggerationof faces by computer. Leonardo 40, 4, 392–400.
[8] KOSHIMIZU, H., T OMINAGA, M., F UJIWARA, T., AND
MURAKAMI, K. 1999. On kansei facial image processing forcomputerized facial caricaturing system picasso. In ProceedingsofIEEE International Conference on Systems, Man, and Cybernetics,
294–299.
[9] CHIANG. P. Y, W.-H. L., AND LI , T.-Y. 2004. Automatic caricature
generation by analyzing facial feature. In roceedings of 2004 Asia
Conference on Computer Vision.
[10] MO. Z, J. P. L., AND NEUMANN, U. 2004. Improved automatic
caricature by feature normalization and exaggeration. In ACM
SIGGRAPH 2004 Sketches, 57.
[11] TSENG, C. C., AND LIEN , J. J. J. 2007. Synthesis of exaggerative
caricature with inter and intra correlations. In Proceedings of The 8th
Asian Conference on Computer Vision, Springer Berlin Heidelberg,
314–323.
[12] TSENG, C. C., AND LIEN , J. J. J. 2012. Colored exaggerative
caricature creation using inter-and intra-correlations of feature shapes
and positions. Image and Vision Computing 30, 1, 15– 25.
[13] CHEN, W., Y U, H., AND ZHANG, J. 2009. Example based caricaturesynthesis. In Proceedings of 22nd Annual Conference on Computer
Animation and Social Agents.
[14] REDMAN, L. 1984. How to Draw Caricatures. Chicago: McGrawHill.
[15] Liang. L, Chen. H, Xu. Y, and Shum. H, “Example-Based Caricature
Generation with Exaggeration,” Pacific Graphics, 2002.
[16] SHET, R. N., L AI, K. H., E DIRISINGHE, E. A., AND CHUNG, P.
W. H. 2005. Use of neural networks in automatic caricature generation:
an approach based on drawing style capture. In IEEE International
Conference on Visual Information Engineering, 23–29.
[17] LIU, J., C HEN, Y., AND GAO, W. 2006. Mapping learning ineigenspace for harmonious caricature generation. In Proceedings of
The 14th ACM International Conference on Multimedia, 683– 686.
[18] LIU, J., C HEN, Y., X IE, J., G AO, X., AND GAO, W. 2009.
Semisupervised learning of caricature pattern from manifold
regularizations. Advances in Multimedia Modeling 5371, 1, 413–424.
[19] Xu. G, Kaneko, M, and Kurematsu. A, Synthesis of facial caricaturesusing eigenspaces, IEICE Trans. D-II, vol. J84-D-II, no. 7, pp. 12791288, Jul. 2001.
[20] Hanaoka. G, Kaneko. M, and Hara. H, Automatic generation of
caricatures based on individual artist styles, IEICE Trans. D-II, vol.
J80-D-II, no. 8, pp. 2110-2118, Aug. 1997.
[21] COOTES.T.F, TAYLOR.C.J, COOPER.D.H, GRAHAM. J, Active
Shape Models - Their Training and Application, Computer Vision andImage Understanding, vol. 61, no. 1, pp. 38-59, 1995.

(a1)

(b1)

(c1)

(d1)

(a2)

(b2)

(c2)

(d2)

(a3)

(b3)

(c3)

(d3)

(a4)

(b4)

(c4)

(d4)

(b) Averaged caricature(c) Caricature with noexaggerations(d) Caricature with exaggerations(a)Figure 10. Results