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Caricature Synthesis with Feature Deviation Matching under
Example-Based framework
Honglin Li · Masahiro Toyoura · Xiaoyang Mao

Abstract Example-based
caricaturesynthesis
techniques have been attracting large attentions forbeing able to generate attractive caricatures of various
styles. This paper proposes a new example-based
caricature synthesis system using a feature deviation
matching method as a cross-modal distance metric.
It employs the deviation values from average features
across diﬀerent feature spaces rather than the values offeatures themselves to search for similar components
from caricature examples directly. Compared withtraditional example-based systems, the proposedsystem can generate various styles of caricatures
without requiring paired photo-caricature example
databases. The newly designed features can eﬀectively
capture visual characteristics of the hairstyles andfacial components in input portrait images. In addition,
this system can control the exaggeration of individual
facial components, and provide several similarity-based
candidates to satisfy users’ diﬀerent preferences.
Experiments are conducted to prove the above ideas.
Keywords Caricature synthesis · Example-based ·Cross-modal distance metric · Feature deviation
matching

1 Introduction
Since the early 1980s, many computer-based methods
have been developed for synthesizing caricatures [1].
These can be roughly classified into photo-transformed
and example-based techniques. Photo-transformed
approaches [2-5, 12-14, 28] achieve caricature styles by
H. Li · M. Toyoura · X. Mao
University of Yamanashi,
4-3-11, Kofu, Yamanashi, 400-8511, Japan
E-mail: {g15dhl01|mtoyoura|mao}@yamanashi.ac.jp

applying certain kinds of image filtering or geometricdeformation to input portrait photos. In these systems,
filters and deformations are usually tailor-designed fora particular style, and hence cannot be generalized
to diﬀerent styles without changing the underlying
algorithms. Example-based systems require a large
number of photo-caricature pairs as example data [6-10,
15, 16, 18, 20]. In principle, the example-based approachhas the advantage that a single framework can beused for generating caricatures of various styles given
corresponding styles of example databases. However,obtaining suﬃcient sets of paired photo-caricature
images is usually diﬃcult. Many existing example-based
systems asked artists to draw caricatures from a large
number of example portrait photos, which is not always
possible in real applications. Recently, approaches using
deep-learning have attracted substantial attention.
While supervised deep-learning approaches [28] may
suﬀer from the problem of requiring even larger
number of photo-caricature pairs than traditional
example-based methods, unsupervised approaches
using the concept of style-transfer-based or image
analogies have also been developed [22-27]. Withstyle-transfer-based systems, it is easy for users toapply a specific form of artwork stylization to theirown photos for sharing and entertainment purposes.
The basic principle of neural-style transferring is toseparate a given style from the content of an image
by considering diﬀerent layers of a neural network.
Since the stylistic information is mainly represented by
low-level textural and color features, these methods are
not suitable for achieving geometric stylization, such as
deforming (exaggerating) the shapes of individual facialcomponents, which is a technique commonly foundin real caricatures. Essentially, deep-learning is an
end-to-end approach, and its existing implementations

2

do not provide users with any control over the detailsof stylization, such as the degree of exaggeration ofindividual facial components.
In this paper, we propose a new example-based
caricature generation technique that can synthesize
stylized caricatures with a small number of unpaired
examples of portrait photos and caricatures. Our
technique incorporates component-specific learning
based on feature vectors that intuitively match thefeatures that people employ to perceive or communicate
the characteristics of faces, which can also provide users
with control over the individual facial components.
While existing component-specific learning methods[6-10, 15, 16, 18, 20] require paired photo-caricature
examples and search for a matching caricaturecomponent via its corresponding photo component, theproposed method searches for the matching caricaturecomponents in their feature spaces directly. However,caricatures of expressive styles will not always provide
an entirely faithful reflection of the features evident insource portrait photos, and hence a direct comparisonof feature vectors between the feature space of
caricatures and the feature space of photographs ismeaningless. To solve this problem, we propose a new
cross-modal distance metric called feature deviation
matching. The key idea is that, given fact that a
caricature is an expressive representation of a person’s
prominent features, the feature spaces of both theoriginal photographs and resulting caricatures should
show strong correlation between these deviations fromtheir corresponding averaged features. Therefore, theextent of deviation from averaged features acrosscorresponding photo and caricature facial component
feature spaces, despite of the modality diﬀerence
between photo and caricature components, can be used
to search for matching facial caricature components
directly under the example-based framework. To
compute the deviation, the proposed method uses
one set of example photos and one set of example
caricatures to learn the distributions of their respective
feature spaces. The images in these two example sets
are not necessarily photo-caricature pairs of the same
persons, and the building of such training sets becomesmuch easier.
In summary, the contributions of this paper are:
1. The newly proposed cross-modal distance metriccalled feature deviation matching technique makes
it possible to generate various styles of caricatures
under the conventional example-based framework
without requiring paired photo-caricature trainingsets.
2. By focusing only on the perceptually prominent
features, the designed feature vectors are robust andHonglin Li et al.

Fig. 1 Comparison of the expressive style synthesized by our
proposed system with those generated by three state-of-art
systems (a sketch example-based system [13], a deep-learning
based photo-transformed system [28] and a component-based
system [20]), A and B columns show the results of the othersystems and our system respectively.

eﬀective for capturing the visual features of input
portrait photos.
3. The proposed system enables users to control the
exaggeration of individual facial components.
Various combinations of individual facialand hairstyle components, based on diﬀerent
exaggeration coeﬃcients and similarity rankings,
can provide users with diﬀerent candidates tosatisfy their particular preferences; this has notbeen achieved in most existing style-transfer-based
and deep-learning-based approaches.

Caricature Synthesis with Feature Deviation Matching under Example-Based framework

Fig. 1 shows the comparison of our synthesized
caricatures of expressive style with three state-of-art
systems (The first and second rows: Comparison withsketch example-based system [13]. The third andfourth rows: Comparison with deep-learning based
photo-transformed system [28]. The fifth to eighth:
Comparison with component-based system [20]). It canbe found that the caricatures generated by the sketch
example-based system and the photo-transformed
system resemble the input portrait photos the best,
but lack artistic feelings. Example or component based
systems can provide various styles of caricatures withartistic feelings. The caricatures by our proposedsystem are competitive with [20] method for similarity
and are comparable with [13, 28] for as an expressive
style.
The remaining part of the paper is organized asfollows: Section 2 reviews related works. Section 3
presents the proposed method. Section 4 demonstrates
some results and describes the evaluation experiments.
Section 5 presents conclusions from the study.
2 Related researches
2.1 Caricature synthesis techniques
Photo-transformed and example-based systems are thetwo main approaches used in early caricature synthesis
systems. Style-transfer-based systems have recently
attracted attention, and produce results that combine
original portrait photos with various styles of example
caricatures.
Photo-transformed systems usually use certain
image processing techniques to transform portrait
photos into caricatures. Gooch et al. [12] succeededin creating caricatures that highlight and exaggerate
representative facial features, by first generating
black-and-white facial illustrations from photographs
and then deforming the facial illustrations. Min
et al. [4] proposed an automatic portrait systemthat leveraged the And/Or graph based on existing
sketch templates. Transductive learning-based facesketch-photo synthesis technique was proposed to
optimize both the reconstruction fidelity of the input
photo (sketch) and the synthesis fidelity of the target
output sketch (photo), which can eﬃciently optimize
the corresponding probabilistic model by alternating
optimization [3]. Recently, Zhang et al. proposed
a content-adaptive method for generating portrait
sketches based on deep-learning techniques under thephoto-transformed framework [28], which can preserve
non-facial factors such as hairpins and spectacles better
than previous techniques.

3

Example-based systems generate caricatures byassembling facial and hairstyle caricature componentsthat resemble the input portrait photo’s into the output
caricature template. Based on Markov random fields[13] and image denoising techniques [14], methods areproposed to create sketches from photos by selecting
the most appropriate neighboring patches to synthesize
a target patch. Chen et al. employed a large databaseof photo-caricature pairs and successfully reflected
personal features in a visually natural rendering by
employing non-parametric sampling techniques [6].
Yang et al. proposed the technique for synthesizing a
caricature by searching a database of teaching pairs,
which can produce further exaggerated eﬀects byadjusting component sizes and positions [18]. Zhang etal. improved the synthesized results of example-based
systems by using machine learning methods to optimize
the combinations and positions of facial components[20].
Style-transfer-based systems impose the visual
attributes (such as color and texture) of the example
caricatures onto input portrait photos to generatevarious corresponding styles of caricatures. Shih et
al. transferred the local statistics of an example
caricature onto an input portrait [22], allowing users
to easily reproduce the visual styles of renowned
artists. Liao et al. used their “Deep Image Analogy”
technique to synthesize target caricatures, whichfinds semantically-meaningful dense correspondences
between the example caricature and the input portrait
photo by adapting the notion of “image analogy” withfeatures extracted from a deep convolutional neutral
network for matching [25]. Fisher et al. performed
non-parametric texture synthesis, which retains moreof the local textural details of the artistic exemplar
compared with other style-transfer-based systems anddoes not suﬀer from image warping artifacts caused byaligning the style exemplar with the target face [26].
Furthermore, the system was able to generate perfectanimation by combining consistent caricatures.
Taigman et al. proposed an unsupervised
cross-domain image generation method based ona domain transfer network (DTN), which can generate
caricatures while preserving the identities of the input
face images [27].
Photo-transformed and sketch-based systems aregood at generating caricatures very similar tothe original portrait photos. Style-transfer-basedand example-based systems are good at generating
caricatures of various artistic styles at the price of losing
some of the original photograph identity. Althougheﬀective in various applications, the above systems haverespective shortcomings. Photo-transformed systems

4

are usually based on style-specific algorithms, andhence cannot be generalized to diﬀerent styles.
Large databases of photo-caricature pairs are required
for the example-based systems-a drawback that
renders them impractical. The caricature synthesis
systems based on supervised deep-learning techniques
require even larger databases of photo-caricaturepairs than traditional example-based approaches.
Using either deep-learning or traditional parametricor non-parametric approaches, style-transfer-based
systems can only transfer texture or color, whereas thestylization of geometric features, such as the shapes of
facial features, are particularly important for achieving
the expressive styles of caricatures.
Based on the newly proposed cross-modal distancemetric, feature deviation matching, our proposedsystem can synthesize various styles of caricatures with
unpaired photo and caricature databases. Comparedwith photo-transformed and style-transfer-based
systems, the proposed system can provide users withcandidate caricatures that have various combinationsof individual facial components based on diﬀerent
exaggeration coeﬃcients and similarity rankings.
2.2 Feature deviation applications and cross-modal
comparisons
Based on the variation within a population of faces,
one could determine an average face [31]. Averageface and facial component features are widely used
for exaggeration control on facial components andareas in caricature generation and other similar
applications. Brenman proposed a widely used rule“Exaggerating the Diﬀerence From the Mean face”(EDFM) and designed the corresponding system called“Caricature Generator”, which exaggerated a graphic
representation of a subject face according to thediﬀerences from an average face computed from aface dataset [32]. Koshimizu et al. defined another
EDFM rule and applied it to their interactive system(PICASSO), which can generate an output caricaturefrom a source image based on certain deviation valueof the source image from the average image [33]. Mo et
al. used normalized deviation from the average model
to exaggerate the distinctive features, based on theconsideration that the DFMs (Diﬀerence-From-Mean)for diﬀerent components are diﬀerent [34]. Xu etal. investigated the borderline between likeness andunlikeness through applying the EDFM rule togradually alternate the face shape at subject study [35].
Cosker et al. learned animation parameters from humanvideo performance and reused them to animate multiple
types of facial model [36], which successfully used

Honglin Li et al.

feature deviation for remapping the facial expression
parameters between diﬀerent appearance models andbetween the appearance models and 3D models. Firstly,
it employed PCA to diﬀerent areas of face image
to generate corresponding features. Then calculated
their deviation values from the corresponding averagefeatures for generating shape-free features (deviation
features) to act as the training data since facial
expressions can be regarded as dynamic changes of
the corresponding facial areas, which can be relatedwith the exaggeration level represented by deviation
values from average features. Although [32-36] usedfeature deviation values to build the distribution offeature spaces or control the exaggeration level of facialcomponents and areas, the feature deviation values
came from a same feature space.
Canonical Correlation Analysis (CCA) has been avery popular method for embedding multimodal data
in a shared space to analyze the linear relation between
diﬀerent modalities [37, 38]. Recently, Deep-learningtechniques are also widely applied in cross-modal
analysis between diﬀerent modalities [39, 40]. Deep
Canonical Correlation Analysis (DCCA) was proposed
to analyze the non-linear relation between diﬀerent
modalities, such as image with audio, image with text,
audio with text, and so on. [41-43] used diﬀerent sub
DNNs composed of fully connected layers to covert
features of diﬀerent modalities into low dimensionalfeatures in a shared D-dimensional semantic space andthen calculate their similarities by CCA function. But
the above methods need to prepare a great number ofdata and require high computational cost.
Traditional paired-example based systems use labels
to relate the example photo components to the paired
example caricature components and then search for thesimilar caricature component by comparing the input
photo components with the example photo component,
which can avoid the cross-modal problem. But it is
not easy to prepare large paired-example databases fordiﬀerent styles of caricatures, which requires diﬀerent
artists’ hard work. To alleviate drawback of thepaired-example based approach, we proposed a new
cross-modal distance metric based on feature deviation
matching, which compares the input photo components
with the example caricature components directly. Whilemost of the existing methods use feature deviation in
one feature space, our proposed method focuses onusing feature deviation for matching across diﬀerent
feature spaces. Although CCA and DCCA methods
are eﬀective to solve the multimodal problems, suchas between image and audio, image and text and so on,they require high computational cost and large dataset.
Our proposed method is much simpler to implement

Caricature Synthesis with Feature Deviation Matching under Example-Based frameworkand eﬀective for the cross-modal matching between
portrait photos and diﬀerent types of caricatures.

3 Proposed method
3.1 Overview
Fig. 2 provides an overview of the proposedsystem. In the oﬄine phase, we use one photographdatabase and one caricature database to learn the
distribution of feature spaces-in other words, tocompute the extent and average of feature vectors
in each feature space respectively, as formulas (4) to(8) show. Feature deviation matching is performed
component-by-component across photo and caricature.
The ASM (Active shape models) algorithm [11] is used
to detect the feature points required for computing thefeature vectors of facial components from both photos
and caricatures. By focusing only on perceptuallyprominent features, the designed feature vectors arerobust against ASM fitting error and can eﬀectively
capture the visual features of the input portrait photos.
However, the fitting results of ASM are instable forsome caricatures occasionally. Fortunately, it is notnecessary to detect the feature points of caricaturecomponents during the online phase. Therefore, we
manually adjust the instable fitting results of ASM tolocate the feature points at the correct positions on
the caricatures. It takes about from several seconds toseveral minutes at most to adjust instable fitting resultsfor each caricature. In the online phase, given an input
portrait photo, ASM is applied to detect the featurepoints required for computing the feature vectors ofindividual facial components from the input portrait
photo. Then, for each component, values are calculated
representing the deviations of actual features fromaveraged feature vectors in their corresponding feature
spaces. By comparing the feature deviation values of
the input portrait points required for computing thefeature vectors of individual facial components fromthe input portrait photo. Then, for each component,
values are calculated representing the deviations ofactual features from averaged feature vectors in theircorresponding feature spaces. By comparing the featuredeviation values of the input portrait photo components
with those of the caricature components, the mostsimilar caricature components are found, as formula (9)shows. After deforming the facial contours of the output
caricature, deciding the component positions, andadjusting their sizes, the searched facial components arecomposited into the output caricature. Details of theprocedures are described in the following subsections.

5

3.2 Feature vectors
There are external and internal features exist in humanportraits. Hairstyle and face shape can be regarded as
the external features, and eye, eyebrow, mouth and noseare considered as internal features. One of the largest
advantages of caricatures is that they can emphasizea person’s most prominent facial features to make iteasy for observers to identify the subject at a glance.
For creating such caricatures, feature vectors should be
carefully designed to capture the prominent features offaces. By observing many types of caricatures, it canbe found that mostly eyebrow and nose components
contain little internal details. The positions of eyeballs
and the status of eyelids and lips are indeed very
important. But in our currently processed caricatures,
most of the eyeballs are in the center. It is better todeal with the status of eyelids and lips by preparing
two sets of corresponding components (closed and open)
and using threshold values like [20] to determine which
set to be used. Our previously designed features in [21]tried to capture the internal and external details offacial components simultaneously. But the experimentresults showed that they usually canceled out eachother. In addition, humans perceive faces based moreon spaces. By comparing the feature deviation valuesof the input portrait characteristic information —small, thin, and drooping, for example — than on
precise shape information. Due to such considerations,
we have designed feature vectors mainly composed ofsimple geometric characteristics that reflect the overall
information of individual facial components. Using
only a minimum set of characteristic features can also
alleviate the negative eﬀect of occasionally inaccurate
ASM fitting results to some extent. The newly designed
features are illustrated in Fig. 3, and the evaluation
results shown in Section 4 are of comparable quality.
Eye: Two angles, form factor, rectangularity, andeccentricity are used to constitute a 5d feature vector{θ1 , θ2 , Form Factor, Rectangularity, Eccentricity} forrepresenting the eyes. After drawing the eye contours
by connecting the corresponding detected ASM featurepoints of eyes, we calculate the areas and perimeters of
the eye contours and the areas of their corresponding
bounding boxes. The lengths of the long and shortaxes of their corresponding smallest circumscribed
ellipses are also computed. Form factor features canbe obtained by using the product of 4π with the eye
contour area, divided by the square of its perimeteras formula (1). Rectangularity is the result of the eye
contour area divided by its corresponding minimum
bounding box area as formula (2). The eccentricity

6

Honglin Li et al.

Oﬄine phase

Online phasel ASM ﬁ#ng

l MatchInput
portrait photol Feature extrac!onl TPS deforml Normalizel Arrange posi!ons
Synthesizel Synthesize
DetectPhoto component
feature spaces

Photo database
ASM
ﬁ"ng

DetectCaricature database
ASM
ﬁ"ng

Map

Arrangeposi#ons

Normalize

Caricature component
feature spaces
TPS deform

Separate

Map

Normalize

Obtainsimilar parts

Fig. 2 Framework of the proposed system

Areaobject
Areabounding−box
AxisLengthlong
Eccentricity =AxisLengthshort
Rectangualrity =

(2)
(3)

Eyebrows, Nose, and Mouth: Eyebrows, nose and mouth
1
are represented by 3d feature vectors {θ1 , θ2 , W
H1 },which are made up of two angles and their width/heightratios.

Fig. 3 Designed feature vectors of facial and hairstylecomponents

value of the eye is calculated by dividing the long axis
by the short axis as formula (3).

F orm F actor =4π × Area
P erimeter2(1)

Hairstyle: Hairstyle is the most influential anddiscriminative component in the facial image. We
use the method in [17] to detect the hair region,
and adopt a feature vector similar to that used in[10] for representing the hairstyle component. Here,the hairstyle is represented by a 120d feature vector{ hfii }(i = 1, 2, · · · , 120) , which is achieved with thefollowing three steps:
Step 1: Deploy hair seed lines.
Step 2: Separate the hair area from the face image via
the watershed algorithm [30].
Step 3: Calculate the 120d feature vectors.
In step 1, as shown in Fig. 4(a), starting with the
ASM points on the upper face contour, we deploy theCaricature Synthesis with Feature Deviation Matching under Example-Based framework

7

Face shape: The proposed system does not regard faceshape as a single component, but instead handles this
together with the hair, as described in the following
section on the hair-contour component.

caricature components are then found according to therelated labels. However, acquiring a large number ofpaired photo-caricature examples is very diﬃcult andhence limits the use of such systems in real applications.
As described in Section 1 and 2, the tendencies in photoand caricature component feature spaces would follow
the same patterns. We propose a new cross-modal
distance metric, namely feature deviation matching, formatching items across diﬀerent feature vector spaces.
Before defining the formulas to compute thedeviation values, the variables were denoted asfollows: feature vector for a facial or hairstylecomponent i ∈ {left eye, right eye, left eyebrow,right eyebrow, nose, mouth, nose, and hairstyle} ofthe input portrait photo as Viin ; the j-th (j =1, 2, · · · , n; n represents the number of photos in thephoto component database) image of portrait photocomponent i in the corresponding photo component
database as Vipho,j ; and the k-th (k = 1, 2, · · · , m; mrepresents the number of caricatures in the caricaturecomponent database) image of caricature component iin the corresponding caricature component database as
Vicar,k . The maximum, minimum, and average feature
vectors of component i in the corresponding photo andcaricature component databases are denoted as V̂ipho ,V̆ipho , V̄ipho , V̂icar , V̆icar , and V̄icar . The normalizedfeature vectors of the input photo components, andthose in the photo and caricature component databases,
are correspondingly denoted as αiin , αipho,j , and αicar,k .
The maximum, minimum, and average values of thenormalized feature vectors are subsequently denoted
as α̂ipho , ᾰipho , ᾱipho , α̂icar , ᾰicar , and ᾱicar . The featuredeviation values are defined as βiin , βipho,j , and βicar,k .
The above variables are then calculated by the following
formulas: Firstly, each dimension in the feature vectors
of facial and hairstyle components is normalized using
formulas (4) to (6) so that all values are within therange 0 to 1.

3.3 Deviation-based feature matching

αiin =

Fig. 4 Procedures for separating hair area from the input
portrait photo and obtaining hair feature vectors

upper hair seed points according to the color diﬀerenceof adjacent pixels. In addition, we also detect thebottom-left and bottom-right hair areas to determine
whether the subject has long hair, especially for female
portrait photos; if so, then the lower hair seed points arealso deployed. The center points of the eyebrows, eyes,
nose, mouth, and the four corner points of the portrait
photo framework act as background seed points. By
connecting the hair seed points and the backgroundseed points separately, we generate seed lines (Fig.
4(b)).
In step 2, after drawing the white seed lines on a
black background image of the same size as the input
portrait photo (Fig. 4(c)), the watershed algorithm isthen used to segment the input portrait photo into twoparts, the hair and background area (Fig. 4(d)).
In step 3, 120 straight lines are drawn, radiatingfrom the center of the face (the bottom point of thenose, detected via ASM, was used in our paper), as
shown in Fig. 4(e). The points at which the straightlines intersect the hair region at each angle are located,and corresponding ratios are calculated (equal to thehair thickness value divided by the distance from thecenter point to the first intersection point).

Even if the proposed system succeeds in calculating
the same feature vectors for the portrait photoand caricature components, it is unlikely that thefeature vectors from diﬀerent feature spaces will
match up. To deal with this cross-modal problem,
traditional example-based systems construct paired
photo-caricature databases. By using labels to relate
the photo components to the paired caricaturecomponents, the feature vectors of the input photocomponents need only be compared with those in
the photo component databases. The most similar

Viin − V̆ipho
V̂ipho − V̆ipho

αipho,j =αicar,k =,(4)Vipho,j − V̆ipho
V̂ipho − V̆ipho
Vicar,k − V̆icar
V̂icar − V̆icar

,

(5)

.

(6)

After normalizing the feature vectors of the input
photo components and those in the photo andcaricature component databases, we calculate theircorresponding feature deviation values with formulas(7) and (8).

8Honglin Li et al.

Fig. 6 Deformation of the caricature face shape using the
TPS algorithm

Fig. 5 Synthesized caricatures with diﬀerent exaggerationcoeﬃcients for eyes or eyebrows

βiin =

αiin − ᾱipho
ᾱipho − ᾰipho,

αcar,k − ᾱicar
βicar,k = i car.
ᾱi − ᾰicar(7)
3.4 Synthesis of resulting caricature(8)

The matching caricature component k̃i in the
caricature component database can be determined via
formula (9).
k̃i = arg min ||βiin − βicar,k ||.

(9)k

By applying formula (9), the proposed systemsearches for the most similar facial and hairstyle
caricature components in the caricature database.
In addition, more exaggerated or averaged
caricatures can also be easily synthesized similarly
to [18] by configuring an exaggeration coeﬃcient informula (9) as follows:k̃i = arg min ||∂βiin − βicar,k ||.

can be applied to the facial components except for
the hairstyle component. Fig. 5 shows five examples
in which eye and eyebrow features generated using
diﬀering coeﬃcients are imposed onto synthesized maleand female caricatures. Each example contains three
caricatures with the exaggeration coeﬃcients set to0.01, 1.0, and 10.0 for eyes or eyebrows separately.

(10)

k

The proposed system then searches for exaggerated
components (where the deviation from the mean islarger) when ∂ > 1, similar components (where there
is small deviation from the mean) when ∂ = 1
and averaged components (where the deviation fromthe mean is smaller) when 0 < ∂ < 1. In thecurrent implementation, the exaggeration coeﬃcient

The proposed system thus synthesizes the output
caricatures with the searched caricature components
via the following two steps.
3.4.1 Deform the output caricature face shapes using
the Thin Plate Spline (TPS) algorithm
Since a large part of a facial shape is actually
represented as the boundary of the hair region, we
treat the hair and face shape as a single hair-contour
component when preparing the example databases. The
search procedure does not attempt to find specificcontours matching the input portrait photos, but uses
the feature vector of hairstyle only for feature deviation
matching. However, it is known that the overall
impression of a face varies considerably according to
the face shape [35]. Therefore, in the synthesis phase,
we deform the face shape of the searched hair-contour
component to match that of the input portrait photo.
The deformation procedure is illustrated in Fig. 6.
At first, we compute the width and height ratiosof the input portrait face over the searched caricature
hair-contour component. According to the computed
ratios, the searched caricature hair-contour component
is resized similarly to the input portrait face. Then,the Thin Plate Spline (TPS) algorithm [19] is applied

Caricature Synthesis with Feature Deviation Matching under Example-Based framework9

to deform the resized hair-contour component so thatthe face contours of the input photo and the output
caricature align with each other. TPS deformation uses
the seven points on the face-bottom contour, which aredetected with ASM fitting.
3.4.2 Arrange the components into the output
caricatureWith the deformed hair-contour caricature component
as the template, we arrange the searched facialcomponents onto it appropriately to create the output
caricature. Before doing so, the proposed systemdelineates circumscribed rectangles on the template forthe individual facial components. The circumscribedrectangles around the corresponding facial components
are defined as the bounding boxes of all the featurepoints of the components. To maintain the relative
sizes and shapes of searched components in the output
caricature, formulas (11) and (12) are applied to
calculate the widths (W) and heights (H) of thecircumscribed rectangles.
Wiout =Wiin× Wfout
ace ,Wfinace(11)

Hiout =

Hisea
× Wiout .
Wisea

(12)

In addition, the proposed system uses the following
predefined rules to make the output caricature facelayout resemble that of the input portrait photo.
1. Define the starting center point of the face.
The proposed system uses the bottom point of thenose as the center point, as this point is always mostcorrectly fitted by ASM.
2. Locate the positions of the upper-left corners of the
facial component rectangles.
To maintain consistency of layouts between the input
portrait photo and output caricature face, the relative
distances among their facial components are calculated
by formulas (13) and (14) (x and y represent
horizontal and vertical distances between individual
facial components).
=
dout,xidin,xi
× Wfout
ace ,Wfinace(13)

dout,y=i

Hiin,y× Hfout
ace .
Hfinace(14)

As shown in Fig. 7, starting from the bottom
point of nose (regarded as the center point), theFig. 7 Procedure for locating positions of facial components

positions of the eyes and mouth can be located by theirdistances from it. Subsequently the eyebrow positions
are deduced from the positions of the eyes. Afterobtaining the final sizes and positions of the synthesized
caricature components, the proposed system deploys
them to the output caricature template. It is possible
to improve the final synthesized results by combing
the method from [20], with which the combinations offacial components and adjustments of positions trainedby machine learning are considered. The method usedin [20] can also determine eyelid types, and whether
subjects are wearing spectacles. In addition, by using a
Gabor filter to detect the texture value of the cheeks inthe portrait photos, and setting a threshold value, it ispossible to determine whether the input portrait shows
an elderly subject. The distance between the upperand lower lips indicates whether a subject’s mouth isopen. Combining the above measures with the proposedsystem may improve the synthesized results in futurestudies.

4 Results and evaluation
4.1 Results
To validate the eﬀectiveness of the proposedcross-modal distance metric, namely feature deviation
matching, and caricature synthesis algorithm, we
conducted experiments with four example sets ofthree diﬀerent caricature styles: a male example
set of expressive style, three female example sets ofexpressive styles, photo-realistic style and drawing
style. For comparison with the method using paired
photo-caricature examples, the male example set isconstructed from paired photo-caricature examples,
although the paired relationship is not used to searchfor similar caricature components in our system. Fig.

10

Fig. 8 Examples of the male caricatures of the expressive
style

Honglin Li et al.

Fig. 10 Some examples of female caricatures of thephoto-realistic style

Fig. 11 Some examples of female caricatures of the drawing
style
Fig. 9 Some examples of female caricatures of the expressive
style

8, Fig. 9, Fig. 10 and Fig. 11 show some results basedon the four diﬀerent datasets respectively.
For practical applications, similarity is not the only
factor considered. That is to say, the most similar
caricature may not be the most desirable, since users’
perceptions of the face can be highly subjective.
In other words, in some cases, the second or third
most similar caricatures may be preferred rather thanthe most similar caricatures provided by the system.
Our proposed system can provide users with diﬀerent
candidate caricatures by controlling the exaggerationcoeﬃcients for corresponding facial components, as
Fig. 5 shows. Furthermore, various combinations withsimilarity-based facial and hairstyle components cansatisfy users’ diﬀering preferences in another way. Fig.
12, Fig. 13, Fig. 14 and Fig. 15 show three candidate
caricatures combined with the searched facial andhairstyle components for each input portrait photo.
Caricatures in column (A), (B), and (C) are synthesizedwith the most similar, second-most, and third-mostsimilar components, respectively. Note that the overall

impression of a caricature is determined not only by
the similarities of the components themselves but also
by their combinations. Training them through machinelearning [20] or other methods may generate caricatureswith more attractive combinations of components. By
providing several similarity-based candidates, it is also
possible to combine this system with the relevance
feedback system based on the OPF (Optimum-Path
Forest) algorithm to improve the synthesized resultsinteractively and iteratively for online training [29].

4.2 Evaluation
4.2.1 Experiment I: Similarity
Experiment I is used to evaluate whether the
proposed system can synthesize similar caricatures
according to the input portrait photos without apaired photo-caricature database and be generalized togenerate various styles of caricatures.
Since the feature deviation is a relative value andits real meaning depends on the distributions of theCaricature Synthesis with Feature Deviation Matching under Example-Based framework

11

Fig. 14 Some examples of female caricatures ofphoto-rea-ist-c style-syn-hesized with the most (left),second-most (middle), and third-most (right) similar
searched components

Fig. 12 Some examples of male caricatures of the expressive
style synthesized with the most (left), second-most (middle),
and third-most (right) similar searched components

Fig. 15 Some examples of female caricatures of the drawing
style synthesized with the most (left), second-most (middle),
and third-most (right) similar searched components

Fig. 13 Some examples of female caricatures of theexpressive style synthesized with the most (left), second-most(middle), and third-most (right) similar searched components

corresponding feature spaces, it is very important thatthe feature spaces of the photo and caricature databases
should have similar distributions for executing thefeature deviation matching. We achieve this bycollecting photos and caricature images with various
types of hairstyles and facial components, avoiding
distribution bias in their feature spaces.
80 female portrait photos, 60 female caricaturesof expressive style, 120 female caricatures ofphoto-realistic style and 100 female caricaturesof drawing style were collected respectively. Ten
participants (6 female, 4 male, all aged in their
twenties) were asked to evaluate the resulting
caricatures generated for 40 randomly chosen female
photos which are not included in the example photo
database. The test photos and the resulting caricaturesof the three styles were presented to each participant
simultaneously side by side as Fig. 16 shows, which

12

Honglin Li et al.

comparison to avoid personal diﬀerence in scoring
metric. The average scores were 3.54, 3.61 and 3.83 forthe expressive and two realistic styles.
The experiment results prove that the proposedsystem can be generalized to synthesize diﬀerent styles
of caricatures similar to the input portrait photos
without paired-example databases.

4.3 Experiment II: Comparison with paired-example
methodExperiment II compares the results of the conventional
paired-example matching method with those of the
proposed feature deviation matching method.

Fig. 16 Some examples displayed to participants inExperiment I (A, B and C represent caricatures of drawing,
photo-realistic and expressive styles respectively)

Fig. 17 Some examples in Experiment II on comparisonof synthesized caricatures based on the proposed featuredeviation matching and the paired-example matching
methods

make participants can compare the caricatures withnot only the input portrait photo but also among
the caricatures themselves. Then participants wereasked to evaluate whether the synthesized caricature
resembles the input portrait photo, using a five-point
scale. To make participants evaluate the results morereasonably, calibrating the average scores is necessary.
They were told score 3 means acceptable in similarity,
1 and 5 represent least similar and most similar, thatis to say, using the score 3 as a base line value for

The comparison should be based on the same paired
photo-caricature database. We therefore performed
this experiment on the expressive style of male
caricatures, which used the paired photo-caricature
database containing 83 male portrait photos and 83
paired caricatures. As mentioned at the beginning of
this section, the paired relationship is not used to search
for the similar caricature components in our system.
Caricatures generated for 40 randomly chosen male
photos were compared between our proposed method
and the paired-example method used in [18].
An additional 15 participants (10 female, 5male, all aged in their twenties) took part inthe comparison experiment. Each tested photo
was displayed accompanied with two caricatures
synthesized by the proposed technique and that usedin [18]. As Fig. 17 shows, caricatures generated by the
proposed method (first row) were compared with thosefrom the paired-example matching method (secondrow). The two caricatures were placed randomly when
conducting the experiments. Participants were asked
which caricature was more similar to the tested photo.
In addition, we also asked them to provide similarity
scores simultaneously as in Experiment I. All the 15
participants evaluated 40 photos and hence there were
600 trials in total, out of which, in 341 trials (56.83%)
the results generated with the proposed method were
evaluated to be better than those generated by
the paired-example matching method. The similarity
scores are 3.82 and 3.66 for our proposed systemand the paired-example system respectively. These
two experiment results confirm that the proposedsystem can synthesize caricatures that are competitive
with those synthesized with traditional paired-example
system.

Caricature Synthesis with Feature Deviation Matching under Example-Based framework

Fig. 18 Some examples in Experiment III on prominentinternal feature capturing

4.4 Experiment III: Capturing prominent features
Experiment III examines whether caricatures generated
by the proposed system can capture distinctive facial
features of the input portrait photos. This experimentevaluates the expressive style with male caricature andthe photo-realisitic and drawing styles with female
caricatures generated by the proposed systems. Fourcaricatures together with input portrait photo weredisplayed for 5 seconds (considering the participants
should compare the similarities not only between the
input portrait photo and the caricatures but also among
the caricatures themselves) to another 15 participants(10 female and 5 male, diﬀerent from participantsin Experiment II). One of the four caricatures was
synthesized by the proposed system, and the otherthree were selected from the corresponding caricature
databases. Each participant evaluated 40 input portrait
photos, giving a total of 600 trials. For each trial, aparticipant was asked to select the caricature mostsimilar to the input photo from the four displayed. The
recognition count increased by one when a participant
selected the caricature synthesized by the proposedsystem.
As mentioned in section 3.2, hairstyle and face shape
are two types of external features for human portraits.
Hairstyle can be regarded as the most influential anddiscriminative component for human vision [44, 45].
Face shape also influences the impression of humanface greatly [35]. The evaluation results may be largely
aﬀected by hairstyles and face shape if the remaining
three caricatures are random chosen like [18, 20, 21].
For example, if the input female photo shows a shorthairstyle, the displayed caricatures with long hair
may be excluded immediately by most participants
no matter how similar the other facial components
are. In order to avoid such factors and evaluate
whether the proposed system can capture the internal

13

facial features from the input photos, we therefore
conducted adjustments on the experiments of [18,20, 21]. Firstly, we randomly selected three candidate
caricatures from caricature categories with hairstyles
similar to the synthesized caricature. Secondly, we
manually adjusted the three candidate caricatures’face shapes to be as same as possible with thesynthesized caricature. As shown in Fig. 18, theupper-left caricature was generated by the proposedsystem, and the other three were randomly selectedfrom corresponding caricature categories with similar
hairstyles and then with their face shapes adjusted.
The four caricatures were randomly placed when
conducting the experiments. As the result, the overall
recognition rates for the expressive style of male
caricature, the expressive style of female caricature, thephoto-realistic style of female caricature and drawing
style of female caricatures were 44.2%, 42.5%, 42.8%
and 52.3% respectively. Binominal tests reveal that all
the recognition rates are significantly higher than theassumed recognition rate (25%, select one randomly
from four) at a significance level of 99% (p=0.01), which
reasonably reflect the internal feature detection abilityof the proposed system.
From the above experiments, it can be concluded
that the proposed system based on the featuredeviation matching method can synthesize facial
caricatures resembling input photos without utilizing apaired photo-caricature database. Using the designed
geometric feature vectors, the proposed system canreliably capture prominent facial features. Even if
a paired photo-caricature database is available, theproposed system can be used as an alternative, since
its synthesized results are comparable with those of
the paired-example system. It is very important toensure that the photo and caricature databases have
similar distributions. In the current implementation, we
achieve this by using various diﬀerent components as
possible. A strict, automatic method would be moredesirable for ensuring better results.
5 Conclusion
We propose a cross-modal distance metric calledfeature deviation matching for example-based
caricature synthesis. With this new matching method,
the proposed system can synthesize caricatures
capturing the prominent features of faces withouta paired photo-caricature database. Diﬀerent styles
of caricatures can also be generated by employing
caricature databases containing corresponding stylistic
examples to the proposed system. Moreover, thedesigned geometric features can eﬀectively capture

14

the prominent features of input portrait photos.
Compared with style-transfer-based approaches, ourproposed system can better satisfy diﬀerent users
by controlling the details of stylization, such as thedegree of exaggeration of individual facial components,
and by providing users with a choice between several
similarity-based synthesized caricatures. In addition, in
future work, the proposed system could be combinedwith the OPF algorithm to improve the synthesized
results interactively and iteratively.
Although the proposed system is robust, flexible,
and easily generalized, the results could be further
improved by using more a sophisticated algorithm, suchas that in [20], in arranging the facial components. As
described in the previous section, we maintain similar
distributions throughout the photo and caricaturecomponent databases by manually confirming that both
databases contain the most diverse examples possible.
We can expect better results by applying a stricter,automated method.
Future work will consider more detailed features,such as single or double eyelids, closed or open
mouths, with/without spectacles, and subjects’ age.
In addition, the machine learning methods for facialcomponent combinations and position adjustment usedin [20] should contribute to the final synthesized
results. Combining the proposed system with therelevance feedback approach can further reflect auser’s preferences. Since the proposed feature deviation
matching method can ignore divergences between thephoto and caricature component feature spaces, it ispossible to make use of this advantage for the inverse
application, i.e., to synthesize photos according to input
caricatures, for example by assisting law enforcement
agencies to match photographs of known criminals
based on hand-drawn caricatures produced by specialist
artists.
Acknowledgements
This study was funded by JSPS Grants-in-Aid
for Scientific Research (Grant No. 26560006 and26240015).
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Honglin Li received the B.Sc.
degree in Automation fromCentral South University and
M.Sc. in Computer Sciencefrom Huaqiao University in2003 and 2012 respectively in
China. He began to study as aPh.D candidate in Computer
Science in University of
Yamanashi from 2015. His
research
interests
includecomputervision,patternrecognition and data mining.

Masahiro
Toyourareceived the B.Sc. degree inEngineering, M.Sc. and Ph.D.
degrees in Informatics from
Kyoto University in 2003,
2005 and 2008 respectively.
He is currently an Associate
Professor at Interdisciplinary
Graduate School of Medical
and Engineering, Universityof
Yamanashi,
Japan.
His research interests areaugmented reality, computerand human vision. He is amember of ACM and IEEE
Computer Society.

16

Honglin Li et al.
Xiaoyang Mao received her
B.Sc. in Computer Sciencefrom Fudan University, China,
M.Sc. and Ph.D. in Computer
Science from University ofTokyo. She is currently aProfessor at Interdisciplinary
Graduate School of Medical
and Engineering, Universityof
Yamanashi,
Japan.
Her
research
interests
include texture synthesis,
non-photo-realistic renderingand their application toscientific visualization. She is amember of ACM SIGRRAPH
and IEEE Computer Society.