2014 International Conference on Cyberworlds

A Study on Perceived Similarity Between
Photograph And Shape Exaggerated Caricature
Jiayi Xu
Hangzhou Dianzi University, China
xujiay@hdu.edu.cn

Wei Yang
University of Yamanashi, Japanxaviera860110@gmail.com

Xiaogang Jin
State Key Lab of CAD&CG,
Zhejiang University, China
Email: jin@cad.zju.edu.cn

Masahiro Toyoura
University of Yamanashi, Japan
Email: mtoyoura@yamanashi.ac.jp

generation, and it has not been quantitatively measured in
the research of face recognition. In the past decades, large
amounts of technology have been developed for generating
caricatures automatically or with user assistance. Automatic
approaches mainly focus on how to detect personalizedfeatures and represent these features as real as possible or
make it more impressive through exaggeration. Interactive
systems leave likeness and exaggeration control entirely
up to users. Without detailed guidelines, it can be very
difﬁcult for a non-professional user to have a control over the
likeness. For example, in Wii games, it is usually very hardto create an avatar similar to a particular person. On the otherside, some researches on face recognition deformed the face
using a caricatured way, and experimented to see whether
face distortion affects the process of human recognition.
The results of [24] and [28] showed that caricatured face
seems to be better recognized than veridical face. Hole etal. [13] found global stretching of face shape in vertical andhorizontal direction may impair human recognition. He concluded that there is no simple measurements between facial
features and recognition. However, the deformation in theirexperiments did not consider the features of the original face,which is different from the case of caricature where a face
is usually deformed to exaggerate its characteristic features.
Although the above mentioned researches found deformationis related to the face recognition performance, to what
extent the deformation will impaired the face recognition
performance, or to what extent of the exaggeration will aid
the face recognition performance hasn’t been addressed.
In this paper, we present several user studies designed
for achieving insight into the control of likeness against
the exaggeration of face shape. Here, face shape means
the overall impression of face contour shape. We chose
face shape because it is known to be the most importantfeature for identifying a person. A person can become hardly
recognizable simply by replacing his or her face shape withthat of another’s. As universally claimed by caricaturists( [22] [25] [12]), the ﬁrst step of drawing a caricature is to

Abstract—This paper investigates the relationship between
the extent of exaggeration in a caricature and its face identiﬁcation ability. As face recognition is largely inﬂuenced by facial
deformations, we focused on ﬁnding the borderline between
likeness and unlikeness by applying gradual alterations to
the face shape of the subject being studied. Suggestions on
manipulating the degree of similarity when generating a
caricature will be given. The experimental environment in thisresearch can be used as a user-friendly caricature generation
system based on “Exaggerating the Difference From the Meanface”, which allows a user to freely control each generation
step and design his or her own unique caricature portrait.
Keywords-AAM; Caricature; Cartoon; EDFM; Exaggeration; Face Recognition; Feature Proportion

I. I NTRODUCTION
Caricatures can be found in various media and situations
in our daily lives because they are expressive, simple,
and amusing. The essence of traditional caricature art isto represent the personalized features of a person in an
exaggerated way. Using caricature portraits, which preserve
key features of the subjects, usually makes it easier torecognize and remember the subjects. In recent years, it
has become more and more popular to use caricatures insocial media and digital entertainment. In social media,
such as Facebook, a subject may post a caricatured faceon their proﬁle page instead of his or her real image.
Personalized avatars in interactive movies and computer
games are caricatures. Nintendos Wii games allow users to
combine facial parts to create the caricature for a character.
One of the most important issues for those applications is
privacy protection. In many cases, instead of generating a
proﬁle as similar as possible to the person, a caricature that
preserves some features of the person but cannot be easily
identiﬁed is more preferable [26]. This raises the issue of
how to control likeness when designing caricatures.
However, to the best of our knowledge, how the degree offace change is related to likeness has not been thoroughly
addressed by existing research on computer-based caricature
978-1-4799-4677-8/14 $31.00 © 2014 IEEE
DOI 10.1109/CW.2014.37

Xiaoyang Mao (Corresponding Author)
University of Yamanashi, Japan
Email: mao@yamanashi.ac.jp213reference points on the “average face”. In EDFM, feature points are moved with an amount proportional tothe difference from the corresponding reference points
and are connected to create a line-drawing caricature.
Koshimizu et al. [15] applied the same idea in theirinteractive system (PICASSO). They deﬁned the EDFM
rule in formula:

sketch the face shape. The famous caricaturist Redman [22]said the face shape is the Sun and the other face elements
are planets to its Sun. Many existing computer caricature
generation techniques are based on such a claim and selectface shape as the most important feature for performing
exaggerations. Some researchers conducted experiments andstated deformation of face shape affects the process of facerecognition [13]. However, none of them has quantitatively
veriﬁed how the exaggeration of face shape affects likeness.
Motivated by the aforementioned research, we built a semiautomatic caricature generation system, which we used asa test bed to conduct an experience that aimed to reveal towhat extent a change in face width and length will affecta caricatures resemblance to the original photograph. Aschange in one feature will have an impact on the otherfeatures, we considered the importance of relative locationsof facial components. We ask the subjects to relocate thefacial components into proper positions after the deformationof face contour. We ﬁnd:
• The borderline of likeness and unlikeness when changing the shape of a female face by elongating andwidening the face contour• The fact that human eyes are more sensitive to change
in face width than face length
In addition to the above contribution, the test bed we built
can be viewed as a user-friendly caricature design system,
which provides free control to the degree of exaggeration.
The aforementioned ﬁndings can be used as a guideline tocontrol likeness.

Q = P + b ∗ (P − S)(1)where P is the source image, Q is the resulting
caricature, S is the mean face, b is the exaggeration
rate and P − S is the difference between the input
image and the mean face. With a piece of caricaturedrawn by the artist and a photograph, Chiang et al. [21]morphed the artist’s work into a caricatured personin the photograph based on EDFM. Mo et al. [31]used normalized deviation from the average model toexaggerate distinctive features. Tseng et al. [29] [30]used both 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. [9] considered the two relative principles
described in Redman’s study [22], and proposed the“T-Shape” rule for emphasizing the relative position
relationship between facial elements. They measured
the similarity between the caricature and the photograph with modiﬁed Hausdorff distance (MHD), andminimized the distance to improve their results.
• Training-based approachThis approach tries to mimic a caricaturist’s drawing style through machine learning methods. Liang etal. [17] attempted to learn the style of a caricatureusing partial least squares. Chen et al. [8] drew thefacial components using a local model, and rearranged
them using a global model based on examples. Shetet al. [27] used cascade correlation neural network tolearn exaggeration degrees of caricaturist. Liu et al. [18]applied principle component analysis (PCA) to obtain
the principle components of the facial features, andthen used support vector regression (SVR) to predicta result for the face provided in the study. They [19]further proposed a non-linear mapping model usingsemi-supervised manifold regularization learning.
In most of the above mentioned caricature generation
literatures, however, how the extent of exaggeration isrelated to likeness hasn’t been quantitatively studied. Ininteractive approach, the exaggeration rate is user speciﬁed.
It usually requires professional skill or rich user experience
to control likeness. In rule-based approach, the valueof b in Equation 1 is set by users, and no guideline
for manipulating likeness is provided. In training-based
approach, the exaggeration styles learned from the examples
are directly applied to the input, and additional likeness
control is not allowed.

II. R ELATED W ORKS
Computer generation of caricatures may be based on verbal descriptions of facial features, or start from an input facephotograph [3]. Since the 1980’s, there has been tremendous
research on the automatic or semi-automatic processing of an
input face image into a caricature. The existing approaches
can be roughly classiﬁed into three categories [26]:• Interactive approach
In this approach, the facial features to be exaggerated
and their exaggeration rates are interactively appointed
by the user. Akleman et al. [2] provided a simple
morphing template for the user to manually deformthe facial features. Later, they improved the algorithmwith a new deformation algorithm that uses simplicial
complex [1]. Gooch et al. [11] converted a photographto a simple line illustration, and then manipulated thefeature grid imposed on the illustration.
• Rule-based approachThis approach simulates the predeﬁned rules to draw
caricatures. One widely used rule is “Exaggerating
the Difference From the Mean face” (EDFM). It wasﬁrst proposed by Brennan [7]. Brennan placed 165
feature points onto the input face image and 165

214

Although some existing approaches noticed that the relative
position of facial components is important for identifying aperson, as mentioned in the rule-based approach paragraph,they selected the features described in the literature [22]and exaggerated each of these features according to thedifferences in the average model. Recently, Klare et al. [14]used crowdsourcing to qualitatively describe 25 featuresand labeled the importance of each feature through machinelearning. They concluded that Level 1 features, including
face length and face shape, were more discriminative
than Level 2 features, including eye separation, nose toeye distance, nose to mouth distance, and mouth to chindistance, in recognizing a person. From this conclusion, wesuggest that the change in Level 1 features has a greaterinﬂuence on likeness than change in Level 2 features. But
as the importance of features was evaluated in a qualitative
way, the relationship between the amount of change andlikeness was not studied, thus limited the applicability of
their research work.
In the research area of face recognition, some researches
have concluded that face recognition is affected bygeometrical distortions of the face, and what is importantin the process of recognition is the difference between
individual face and the average face [23] [4] [5]. For
example, Berger et al. [6] simpliﬁed the stokes andgeometric shape of a face image in levels, and an
abstraction of the face occurred. As can be inferred fromthe result, even using only a few sketches, human eyes canstill relate the portrait to the original image. To discover
face identiﬁcation performance under face deformation,
Hole et al. [13] designed three experiments to learn whether
or not stretching a face horizontally or vertically will impair
the face recognition process. In experiments 1 and 2, global
linear transformations in vertical and horizontal directions
were applied to the entire image. By conducting a user
study, the authors concluded that vertical and horizontal
stretching have little impact on face recognition. It is
unclear why they chose to stretch the face in verticaland horizontal directions instead of along the feature
vector. Feature vector is usually a high dimensional vector
represented by feature points. It was not explained why
the tested images were stretched to 150% and 200%. In
experiment 3, either the top or the bottom half of the face
was stretched vertically twice to its original height, leaving
the other half untouched. In this case, face recognition was
found to be impaired. It was not explained how the extent
of this non-global vertical deformation is related to facerecognition performance. Sinha et al. [28] presented 19
basic results about the face recognition process. Amongthe 19 results, they pointed out that “face-shape appears
to be encoded in a slightly caricatured manner”. Morespeciﬁcally, they declared the rule that caricatured departurefrom veridical face performance better in human facerecognition process.

Figure 1. (a) The “average face” labeled with feature points. (b) The
corresponding caricature.

1
5
6

1

2

2

3

3
4

Figure 2.

4

Features about face width and length.

III. B UILDING OF E XPERIMENT S YSTEM
A. Caricature Generation
Generating a caricature relies on capturing face features.
In this paper, we described 82 facial feature points. As shownin Figure 1(a), we placed 22 points around the face contour,
7 points around each eyebrow, 11 points around each eye, 13
points around the nose, and 11 points around the mouth. Hairis currently removed to eliminate the effect of hairstyle. For agiven frontal face image, the feature points are automaticallylocated using active appearance model (AAM) [10] trainedby 30 photographs of young Asian females. We interpolated
each group of feature points using piecewise Catmull-Rom
spline, which passed through all the points. We did thisto obtain a simple yet smooth geometrical description offace contour and each facial component. The result of theperception experiments might differ somewhat depending onwhether cartoon faces with color information or line-drawn
faces are used [23] [4] [5]. To obtain more information fromthe original image, a cartoon style was created by ﬁlling
face, eyebrows, pupils, and lips with colors sampled fromthe photograph. An example of a rendering result is shownin Figure 1(b).
B. Exaggeration of Face Shape
We focused on the exaggeration of face shape withwidening and elongating operations. We exaggerated theface contour with an amount proportional to its difference
from the “average face”. The more a feature deviates

215

Figure 3.

The procedures for widening the face.

Figure 4.

The procedures for elongating the face.

Table I
FACE SHAPE FEATURES

from that of the “average face”, the more it is emphasizedin the resulting caricature. In this paper, the “averageface” (Figure 1(a)) is generated by ﬁrst normalizing andthen averaging the photographs used in training AAM.
Examples of exaggerated faces are shown in Figure 5. In
the following, we will ﬁrst introduce the deﬁnition of facefeatures, and then explain the detailed exaggeration steps.

Face width in eye line
Cranium width
Face width in nose line
Face width in mouth line
Chin width

Table IIR ELATIVE POSITION FEATURES

1) Features: As suggested by Redman [22], we usedwidth at ﬁve different heights of the face as the personalizedfeature of face shape. These ﬁve widths are width in eye line
w, cranium width w1 , width in nose line w2 , width in mouthline w3 , and chin width w4 (Figure 2). To measure the relative positions between facial components, we calculated the
distance between pairs of facial components. For horizontal
directions, we computed the distance between two eyes w5and between two eyebrows w6 . For vertical directions, we
computed the space between eyes and eyebrows h1 , space
between eye and nose h2 , space between nose and mouthh3 , and space between mouth and bottom of the face h4 .
Because each input face image is of a different size, wedeﬁned the features in the form of ratio instead of value. The
currently deﬁned face shape features are listed in Table I,
and relative position features of facial components are listed
in Table II.
2) Exaggeration: The exaggeration steps for stretching
face width are illustrated in Figure 3. To widen the face bya factor of m, we ﬁrst increase the face width in eye line w
to w with the factor m:w = w(1 + m)p= whp1 = wh1
w2
p2 = hp3 = wh3
p4 = wh4

Distance of two eyebrowsDistance of two eyes
Space between eyes and eyebrowsSpace between eyes and nose
Space between nose and mouthSpace between mouth and bottom of the facep11 = ww5
p12 = ww6
p21 = hh1
p22 = hh2
p23 = hh3
p24 = hh4

accordingly. We calculated the normalized deviation n of w
from the “average face” by Equation 3 and the normalized
deviation ni of wi from the “average face” by Equation 4.
The change of width wi is an amount proportional to theratio of deviation ni to n as given in Equation 5.
n

=

ni

=

p − pav
pi − pai
via

(3)
(4)

ni
(5)
, t1 , t2 ))nwhere pai is the mean of pi , and via is the covariance ofpi . pai and via were calculated using the 30 aforementioned
wi

(2)

Then the other widths wi (i = 1, . . . , 4) were scaled to wi ,216=

wi (1 + m ∗ clamp(

Figure 6.
Figure 5.

Interface of our experiment system.

Examples of exaggerated faces.

Asian females. The 21 photographs used in the experimentwere collected from volunteers, free download site, and the
databases of [16] and [20]. The interface of our caricature
generation system is shown in Figure 6. With each clickof the “elongate” or “widen” button (in the yellow box), theface is elongated or widened by 5%. By clicking the buttons
shown in the blue box, the eyes and eyebrows are moved
inward or outward, and the eyes, eyebrows, nose, and mouth
are moved upward or downward. In our experiment, thephotograph (rendered in the left window) and exaggerated
result (rendered in the right window) are shown to thesubjects at the same time.
The subjects who participated in the experiment were university students between 20 to 30 years old. Before all threeexperiments, we ﬁrst taught them how to interactively stretch
the face length and width and adjust the relative positionof facial components. They were given 5 minutes to try thesystem. During the tests, the photographs and corresponding
caricatures were simultaneously displayed on a 24-inch PC
monitor. The participants were told there was no time limit
for the tests.

images in the database. Function clamp() restricted thechange within a reasonable scope [t1 , t2 ]. The distance ofeyebrows w5 and eyes w6 were adjusted to keep relative
position features p11 and p12 .
The exaggeration steps for stretching face length are illustrated in Figure 4. We ﬁxed the position of the nose, andused point K = 12 (P6 +P16 ) (Figure 1(a)) as the origin point
for deformation. To increase the face length by a factor ofm, point k above the origin point is raised to point k :

k = k + m(ky − Ky )(6)
point k below the origin point is lowered to point k :

k = k + m(ky − Ky )(7)

We further emphasized the widths of face contour wi(i = 1, . . . , 4) if they were more distinctive than featurew, according to Equation 8:ni −n
wi (1 − m
4 min(abs( n ), t)) (ni < n)wi =mwi (1 + 4 min(abs( nin−n ), t)) (ni > n&ni > 0)(8)Function abs() calculates absolute value, and function
min() limits the maximum amount of change up to t.
Finally, we adjusted the height of eyebrows, eyes, and mouth
to keep relative position features p21 , p22 , and p23 .

A. Experiment 1: Change the Face ShapeIn this test, the subjects were ﬁrst asked to elongate faces
until they thought the images did not resemble the original
photographs. This was performed using the exaggeration
function in the system described in Sect. III-B2. Secondly,
They were asked to widen faces until they thought they did
not resemble the original people. We counted the number ofsubjects who thought the deformed faces remained similar
to the photographs for each 5% of deformation. Eighteen
subjects participated in this test. We collected 375 samples
from this test. The percentage of subjects who thought theexaggerated faces remained similar to the photographs islisted in Table III.
Discussion
After analyzing the data, we found that the subjects weremore sensitive to the change in a face’s width than achange in a face’s length. In Table III, 10% of elongatedfaces were thought to be similar to the original photographs
by more than half of the subjects. When the faces wereelongated by 15%, the number of subjects who thought

IV. E XPERIMENT AND A NALYSIS
We conducted three experiments in an aim to reveal how
gradual change in face width and length inﬂuence likeness.
In Experiment 1, the subjects were asked to deform theshape of their face in horizontal and vertical directions. In
Experiment 2, they were asked to adjust the position of facialcomponents accordingly after the face shape was deformed.
To investigate whether the result of Experiment 2 is related
to the aspect ratio of faces, as in Experiment 3, we asked
the subjects to classify these faces into long, average, andwide shapes.
The relationship between likeness and the extent of exaggeration might change depending on ethics, age, andsex. In this work, we limited our investigations to young217the caricatures were similar to the original photographs was
reduced to 30.8%. The number of subjects who thought thatthe deformed caricatures were similar to the original faces
was lower than 40%. With a 20% widening of the faces, one
in 375 subjects thought the widened faces were similar tothe original faces. To help ensure that widened faces remainsimilar to the original face, the change should be within
10%. For elongated faces, the borderline of likeness andunlikeness was between 10% and 15%.

Table IIIR ESULT OF E XPERIMENT 1: P ERCENTAGE OF SUBJECTS WHO THOUGHT
THE STRETCHED FACE IS SIMILAR TO THE PHOTOGRAPH .

hhhhhDeformation
hhh degree
hhhh

Direction
VerticalHorizontal

+ 5%

+ 10%

+ 15%

+ 20%

99.6%
86.9%

81.2%
36.3%

30.8%
5.6%

4.4%
0.3%

Table IVR ESULT OF E XPERIMENT 2: P ERCENTAGE OF SUBJECTS WHO THOUGHT
THE STRETCHED FACE IS SIMILAR TO THE PHOTOGRAPH .

B. Experiment 2: Change the Face Shape with the Adjustment of Facial Components’ Position
In Experiment 1, the position of facial components was
adjusted in the system after changing face shape using therule explained in the third step of Figure 3 or Figure 4.
To keep the relative position features shown in Table II
when the participants elongated the face, eyebrows, eyes,
nose, and mouth were moved in a vertical direction, which
automatically maintains relative position features. When theparticipants widened the face, eyebrow distance and eye
distance were modiﬁed automatically to keep features p11
and p12 . However, these adjustments might not lead tothe most similar result perceptually. To eliminate a factor
related to the position of facial components, we conducted
Experiment 2. First, each subject was asked to elongate
a face by 5% each time, and adjust the height of eyes,
eyebrows, nose, and mouth to create the best combination
until they could not produce a caricature that was similar
to the original.Second, each subject was asked to widena face by 5% each time and adjust the distance between
each eye and each eyebrow to create the best combination
until they could not produce a caricature that resembled
the original. Eleven subjects participated in this test. We
counted the number of subjects who thought the deformed
face was similar to the photograph in each step. We collected
229 samples from the tests. The percentage of subjects who
thought the exaggerated face was similar to the photographis listed in Table IV.
Discussion
After comparing the data, the number of subjects who
thought that the caricatures were similar to the original
faces increased when the positions of facial components
were allowed to be adjusted by 5% to 10%. The borderline
remained the same in Experiments 1 and 2. In both cases,
the results showed that there was more resistance to likenessin face length changes than face width changes.

hhhhhDeformation
hhh degree
hhhh

Direction
VerticalHorizontal

+ 5%

+ 10%

+ 15%

+ 20%

99.5%
98.7%

85.4%
39.7%

28.5%
4.8%

4.3%
0%

elongating operation than other face shapes? Is a wideshaped face more resistant to the widening operation thanother face shapes? To answer such questions, we conducteda subject study asking the subjects to classify the faces
into three categories: long faces, average faces, and wide
faces. As different subject had different opinion of whethera face is roughly the average length, longer, or shorter,
after randomly interviewing ﬁve subjects that participated inExperiment 2, we selected generally agreed six long faces,
four average faces, and three wide faces. We reanalyzed thedata collected in Experiment 2. We classiﬁed the test data ofExperiment 2 into 77 samples for the case of long faces, 43
samples for the case of average faces, and 33 samples for thecase of wide faces. The percentage of subjects who thought
the exaggerated faces were similar to the photographs islisted in Table V and Table VI.
Discussion
We found that the results were consistent with those in
Table IV because the subjects were less sensitive in all three
categories to elongating the faces compared to widening thefaces. The highest percentage of subjects believed widened
faces were similar to the original photographs for the longfaces. This conﬂicts with the conventional assumption thatan exaggeration toward the direction of deviation from anaverage model is always the most desirable one for better
depicting a person’s features. Long faces are more resistant
to change than other face shapes when widened or elongatedwithin a factor of 10%.

C. Experiment 3: Subjective Classiﬁcation of Face Shape

V. C ONCLUSION AND F UTURE W ORK

In Experiment 1 and Experiment 2, the face resists deformation more in the horizontal direction than the verticaldirection. However, it still raised the concern about whether
the difference between the elongating operation and widening operation is due to the subjective perception of aspectratio of faces. Is a long-shaped face more resistant to the

In this paper, we investigated how the change in faceshape and the position of facial components inﬂuence thesimilarity of a caricature through a subject study. The
experiment system released in this research work can also
be utilized as a semi-automatic caricature generation system,

218

Table V
R ESULT OF E XPERIMENT 3: P ERCENTAGE OF SUBJECTS WHO THOUGHT
THE ELONGATED FACE IS SIMILAR TO THE PHOTOGRAPH .

` ```` Length
Face shape ````Long face
Average face
Wide face

+ 5%

+ 10%

+ 15%

+ 20%

100%
97.7%
100%

89.6%
83.7%
81.8%

35.1%
23.3%
30.3%

5.2%
4.7%
6.1%

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Table VIR ESULT OF E XPERIMENT 3: P ERCENTAGE OF SUBJECTS WHO THOUGHT
THE WIDENED FACE IS SIMILAR TO THE PHOTOGRAPH .

` ```` Width
Face shape ````Long face
Average face
Wide face

+ 5%

+ 10%

+ 15%

+ 20%

98.7%
95.3%
97.0%

45.5%
27.9%
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3.9%
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0
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0

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which provides users with control of likeness under quantitative guidelines.
The rendering style of caricature may affect the likeness ofa caricature. In future work, we will experiment with caricature in other rendering styles, such as line-drawing style.
Although face shape is considered to be the most importantfeature, the exaggeration of other facial components should
also affect likeness. Therefore, we will continue conducting
experiments to investigate the effect of exaggerating facialcomponents. In the current study, we do not consider theinﬂuence of hair on the recognition of a person. In the
future, we will investigate whether a change in hair style
has a strong inﬂuence on the likeness of a caricature. We
will further extend our experiments to males, and explore
whether or not the same rules are applicable to both males
and females.

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ACKNOWLEDGEMENTS[15] H. Koshimizu, M. Tominaga, T. Fujiwara, and K. Murakami,
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This research was supported by the Natural Science
Foundation of Zhejiang Province, China (No. Q12F020007),the Zhejiang Provincial Education Department of China (No.
Y201121352), and the Zhejiang Provincial Key Innovation
Team on 3D Technology, China (No. 2011R50009).

[16] H. Li, G. Liu, and K. N. Ngan, Guided face cartoon synthesis.
IEEE Transactions on Multimedia, 13(6), 1230–1239, 2001.

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