Session: Attractiveness Computing in Multimedia

MMArt&ACM’18, June 11, 2018, Yokohama, Japan

Modeling the Relation between
Skin Attractiveness and Physical Characteristics*
Kensuke Tobitani

Tatsuya MatsumotoResearch Center for Kansei Value Creation/ School of Science and Engineering
Kwansei Gakuin Uiversity
Gakuen, Sanda-shi, Japan
tobitani@kwansei.ac.jp

Research Center for Kansei Value Creation/ School of Science and Engineering
Kwansei Gakuin Uiversity
Gakuen, Sanda-shi, Japantatsuya0809@kwansei.ac.jp

Yusuke Tani

Noriko Nagata

Research Center for Kansei Value Creation/ School of Science and Engineering
Kwansei Gakuin Uiversity
Gakuen, Sanda-shi, Japantani.y@kwansei.ac.jp

Research Center for Kansei Value Creation/ School of Science and Engineering
Kwansei Gakuin Uiversity
Gakuen, Sanda-shi, Japannagata@kwansei.ac.jp

ABSTRACTCCS CONCEPTS

There is a wide range of requirements for representing skinquality in various fields, such as computer graphics andcosmetics. However, accurate representation of skin quality is adifficult technical issue, because of the complex physicalcharacteristics of skin and its constituent substances andstructure. The objective of this study is to clarify the latent
factors in the impression of skin, mainly skin attractiveness andto provide systematic modeling of the relation between
impression and physical characteristics. This will not only enableintuitive representation of skin quality, but will also help
elucidate the human recognition mechanism in relation to skin.
To build the model, we first select words from those that are
generally used to assess skin. Next, we study techniques capableof accurate skin representation, based on the physicalcharacteristics of skin, and create CG images of skin. We thenuse the words and images to clarify the latent factors for forming
a visual impression of skin. Finally, we conduct a regression
analysis on the results obtained in the subjective evaluation test
to build a systematic model to estimate the impression evoked,
based on the skin’s physical characteristics. This makes it
possible to estimate impressions from physical characteristics
and conversely, to estimate those specific physical characteristics
that contribute to a desired impression.

• Computing methodologies → Computer graphics →Graphics systems and interfaces → Perception

KEYWORDS
Skin attractiveness, prediction model, biophysically-based model,
subjective evaluation test
ACM Reference format:
Kensuke Tobitani, Tatsuya Matsumoto, Yusuke Tani, and Noriko Nagata.
2018. Modeling the Relation between Skin Attractiveness and PhysicalCharacteristics. In Proceedings of International Joint Workshop on
Multimedia Artworks Analysis and Attractiveness Computing in
Multimedia, Yokohama, Japan, June 2018(MMArt&ACM'18), 6 pages.
DOI: https://doi.org/10.1145/3209693.3209699

1 INTRODUCTION
Humans are intimately aware of faces and skin quality, using
them clues to estimate age, gender, health, and emotions [1]. It isalso an important field of study from both academic andpractical perspectives. Thus, it is a matter of great interest to us.
Realistic representation of skin quality is widely sought afterin fields such as computer graphics (CG) and cosmetics.
However, because skin quality is determined by complex
physical factors, including pigments (e.g., melanin andhemoglobin) and multi-layered surface structures andmicrostructures, accurate texture are a difficult technicalproblem.
Our objectives are to clarify the latent factors of skin
impression and to provide systematic modeling of the relation
between impression and physical characteristics. This model will
make it possible to estimate the physical features contributing toskin impression and will enable intuitive rendering of skin

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https://doi.org/10.1145/3209693.3209699

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Our objective is to systematically model the relation between
skin impression and its microscale physical characteristics. To
this end, for evaluation terms, we use words that are suitable forrepresenting skin quality and impression. These words arecollected and selected through a practical experiment. Moreover,
we conduct a psychological experiment and perform statisticalprocessing, using CG images chose for specific physicalquantities of melanin and hemoglobin.

quality. This promises to simplify skin quality representationand to help elucidate the human recognition mechanism, in
relation to skin. Furthermore, it has potential for industrial
application, such as cosmetics development, which also creates
positive impressions of skin.
To create a prediction model for skin quality that systemizes
the relation between impression and physical characteristics, wefirst select words (i.e., evaluation terms) from those generally
used to assess skin quality. We look for words that are suitable
for skin evaluation. Then, we study techniques capable ofaccurate skin representation, based on its physicalcharacteristics. Then, we create CG images of the skin. The
acquisition of a high-accuracy model requires that CG images beboth comprehensive and representative. Using the selectedevaluation terms and CG images, a subjective evaluation test is
conducted. By conducting a factor analysis of the obtained
results, the latent factors, forming a visual impression of skin,are clarified. Lastly, modeling is performed through a regression
analysis of the relation between impression factors and physicalcharacteristics. Thus, we realize a technique for estimating
impression from physical characteristics. Conversely, we realize
a technique for estimating the physical quantity contributing to
the desired impressions.

3 SELECTION OF SKIN EVALUATION TERMS
To model the relation between the visual impression of skin andits physical characteristics, we conduct a subjective evaluation
test of skin images and examine the correlation with the physicalfeatures of the skin in images. To adequately ascertain the makeup of skin impression, words that are comprehensive andrepresentative (i.e., evaluation terms) are selected from words
generally used to describe skin states. In this research, weselected evaluation terms through some tests with statistical
validity.
Table 1: Representative Terms (Low-order impression)

2 PRIOR RESEARCH
The visual impression of skin can be divided into two areas:
impressions from mesoscale shape characteristics, such as
wrinkles or pores and those from microscale physicalcharacteristics, such as melanin and hemoglobin, affecting
optical characteristics. In studies of the skin’s mesoscale shapecharacteristics, Igarashi et al. proposed an eigenspace filtering
technique to generate images with suppressed mesoscale shapecharacteristics for application in cosmetics development, andthey reported on its usefulness [2]. Furthermore, Arakawa et al.
proposed a new non-linear filtering technique and achieved
wrinkle and spot elimination on facial images [3]. However,
because mesoscale shape characteristics evoke a negative
impression of the skin, in most cases, these studies did not reporton the importance of impression. Therefore, for utility, our study
will not look at mesoscale shape characteristics.
In studies of the effects of microscale physical characteristics
of skin, studies have reported that blood circulation, melanin,
and beta-carotene impact healthy appearance [4,5]. Other studies
examined types of physical skin characteristic impressions, suchas “translucent feel,” “dewy feel,” and “shininess” [6-8]. Many
studies clarified the physical characteristics required to achieve“pearly skin.” [9,10] However, most of those studies focused onbrightness distribution in skin images, or they modeled therelation between L*a*b* value variation and impression. Few
studies clarified the correlation between impression andvariation in concrete physical quantities of melanin or
hemoglobin. Moreover, no studies, to date, have clarified theoverall make-up of skin impression, nor have they proposedsystematic models expressing correlation with physicalcharacteristics.

Table 2: Representative Terms (High-order impression)

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MMArt&ACM’18, June 11, 2018, Yokohama, Japan

4.2.1 Melanin. Melanin is the pigment that affects thedarkness of skin. The differences in skin color between black,white, and Asian people are determined by their melaninconcentration and by the ratio of eumelanin and pheomelanin in
relation to the amount of melanin. To reveal the difference in
impression caused by changes in the amount of melanin, we
create experimental stimuli by increasing the amount of melaninin five steps of 9 %, from 0 – 36 %. For the ratio of eumelanin andpheomelanin, we use data on the skin of Asian people [13], asmeasured by Hennesy et al. To exclude the effects of suntans in
the creation of the experimental stimuli, we use pre-suntan
measured averages of eumelanin 94 % and pheomelanin 6 %.
4.2.1 Hemoglobin. Hemoglobin exists copiously in blood, andis the main pigment accounting for the redness of the skin. To
reveal the changes of impression with the changes inhemoglobin, experimental stimuli images are created byincreasing hemoglobin in five steps of 5 %, from 0.1 – 20.1 %.
Furthermore, based on the ratio of blood flowing through thearteries and veins, we set the ratio oxygenated hemoglobin to 64% and deoxygenated hemoglobin to 36 %.
4.2.3 Bilirubin. Bilirubin is a green pigment produced bymetabolizing hemoglobin. Normal values of bilirubin are 0.009 –0.019 g/l, and, if exceeding 0.025 g/l, it is recognized as jaundice.
The present study sets the amount of bilirubin at the center ofthe normal range, 0.014 g/l.
Absorption coefficients for each spectrum in the layers of
the skin used for rendering are determined by the amount ofthese skin pigments. Therefore, it is chiefly represented as skincolor data in the rendered CG images.
4.2.4 Shape properties. In the present study, shape propertiesof the skin are defined as the height of microsurface unevenness,Rz, and thickness of each skin layer, d. Because these parameters
determine shape properties uniquely specified by age [12,14],
values are used for the experimental stimuli images for the agesof 30, 55, and 80. The parameters used for rendering are listed inTable 3.

Furthermore, based on the findings by Nagai et al., thecognitive process for material texture is structured in ahierarchical manner [11]. Thus, we categorized selectedevaluation terms, by mutual consent, into “low-order evaluation
terms,” evaluating the skin’s physical characteristics, and “highorder evaluation terms,” evaluating the emotion evoked by theskin’s quality.
Moreover, by applying cluster analysis, representativeevaluation terms (i.e., representative words) used in thesubjective evaluation test. The representative words for loworder and high-order impressions are listed in Tables 1 and 2,
respectively.

4 CREATION OF CG IMAGES BASED ON
SKIN’S PHYSICAL CHARACTERISTICS
The systemization of the relation between skin’s physicalcharacteristics and impression requires us to conduct a
subjective evaluation experiment for skin images and to study
the correlation with the physical features of the skin in thoseimages. Accordingly, when using photographs of skin as stimuli,
the physical features of the skin must be measured each time.
However, accurate measuring of physical features, such as theamount of melanin, hemoglobin, or layer thickness, is extremely
difficult. Moreover, because physical features cannot be
controlled when using photographs as stimuli, there is an issue
regarding the comprehensiveness of the experimental stimuli.
We solve this by creating CG images of skin, instead ofphotographs, for experimental stimuli. Their physical features
are controlled.
This section describes the technique to create CG, based onskin’s physical characteristics, and the physical features of skin
to control when creating image stimuli.

4.1 Creation method of CG images as
experimental stimuli
Because accurate quality representation is required for ourstudy, we employed the biophysically-based model created by
Iglesias-Guitian et al., performing simulation based on the skin’s
physical features [12]. This technique allows the calculation ofparameters needed for skin-rendering, including absorption andscattering coefficients and layer thickness, based on the skin’s
thickness and pigmentation. These are common parameters in
the fields of medicine and biophysics. Furthermore, theintroduction of data on skin thickness and pigmentation by age
enables us to represent changes in optical characteristics of theskin with ageing.

Table 3: Shape property parameters

4.2 Physical features of skin

4.3 Rendering results

In terms of physical features that affect skin quality, we focus onchanges in melanin and hemoglobin content. These aremicroscale physical features having evident visual effect. It
varies based on changes of skin thickness and micro-surface
unevenness substantially smaller than mesoscale shapes (i.e., a
few mm). This section describes each physical feature required
for rendering the experimental stimuli.

Using the technique and parameters described in the previous
section, 75 types of skin images are rendered (5 grades ofmelanin x 5 grades of hemoglobin x 3 grades of shapeproperties). For the skin model, a model created using 3D data
[15] is used, and the renderer is the physically-based PBRT [16].
Because changes in appearance of an object via changes in the
direction of observation is believed to be an important factor in32

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angles. The frontal direction of this model data is taken as thestandard, and by rotating the viewpoint (i.e., camera position)around the vertical axis at 5° intervals between -10° and +10°.
Five skin CG images are obtained, each from a different viewing
angle. They are taken as one image series set. For stimuli, allimages are shown for 0.5 s in the following order: 10°, -5°, ±0°,+5°, +1-°, ±0°, and -5°. Furthermore, to exclude the impact on
facial features impression, images are trimmed to 300 x 300
pixel-sized squares of just the cheek alone. Fig. 1, 2, and 3 show
the 75 images used in the experiment.

5. SUBJECTIVE EVALUATION EXPERIMENTThis section describes the subjective evaluation experiment thatused CG skin images rendered from skin’s physical features as
experimental stimuli and their selected evaluation terms.

Figure 1: Changes in skin quality
through changes in amounts of melanin and hemoglobin(shape properties: 30 years)

5.1 ParticipantsParticipants in this experiment were 10 university students (6males, 4 females) in their 20s, considered to be of an age groupthat spends more money on beauty products and services thanelderly generations and to have a high level of interest in skin.

5.2 Experimental environment
Experimental stimuli comprised 75 types of image series sets,shown on a monitor one type at a time. The background color
was set to 18 % gray. The experiment was conducted in a darkroom, and the monitor was color-calibrated for each test. The
image sets were repeated until responses were complete.

5.3 Experimental procedure
Participants graded each representative term in relation to eachstimulus on a scale of 1 to 7: 1, applies very much; 2, applies; 3,applies somewhat; 4, cannot say either way; 5, applies little; 6,does not apply; and 7, does not apply at all. Additionally,
participants were instructed to assess their intuitive impressionof the images they were shown during the experiment.

Figure 2: Changes in skin quality
through changes in amounts of melanin and hemoglobin(shape properties: 55 years)

5.4 Results
A factor analysis using the maximum likelihood method andpromax rotation was applied to the averages for each evaluation
item in the 10 subjects’ obtained data. To determine the numberof factors, the reference was set at an eigenvalue of 1 or higher.
The factor analysis resulted at the third factor for low-orderimpression with cumulative contribution ratio of 74.054 % andeigenvalue, 1.474. For high-order impression, up to the third
factor was extracted. However, because commonality for“grown-up” was close to 0, the term “grown-up” was excluded. A
factor analysis was then applied to the 14 remaining terms. The
results were a cumulative contribution ratio of 78.882 % up to thesecond factor and an eigenvalue of 1.06.

Figure 3: Changes in skin quality
through changes in amounts of melanin and hemoglobin(shape properties: 80 years)

5.5 Discussion

the recognition of skin quality [17], participants in the subjective
evaluation experiment are not shown single still images. They
are instead shown a series of images from different observation

For low-order impression, high-loading factors were indicatedfor the first factor with representative terms, such as “greasy,”

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MMArt&ACM’18, June 11, 2018, Yokohama, Japan“oily,” and “heavy,” for the second factor with representative
terms, such as “white,” “translucent,” and “radiant.” For the third
factor, representative terms, such as “moist” and “dewy,” wereindicated. Accordingly, the first factor was taken as theimpression in relation to fat of the skin (i.e., sticky feel); thesecond factor was taken as the impression in relation totranslucency (i.e., translucent feel); and the third factor was
taken as the impression in relation to the amount to moisture in
the skin (i.e., moist feel”). For high-order impression, highloading factors were indicated for the first factor withrepresentative terms, such as “neat and clean,” “beautiful,” and“clear.” For the second factor with representative terms as“florid” and “lively” were indicated. Thus the first factor was set
as “beautiful feel,” and the second was set as “vital feel.”

Table 4: Adjusted R-squared in the systematic models

good degree of accuracy from the coefficients of determination.
The systematic model of impression and physical characteristics
based on these results is shown in Fig. 4, and adjusted R-squaredin each model is shown in Table 4.

6.2 Discussion
6.2.1 Low-order impression. the results of the multiple
regression analysis confirmed that “sticky feel” was explained by
the three physical characteristics of shape characteristics,
amount of melanin, and amount of hemoglobin. Because theshape characteristic coefficient has a positive value and theamount of melanin and hemoglobin have negative values, thedarker the skin color and the stronger the specular reflection
component, the stronger the sensation of “sticky feel.” This may
be because the specular reflection component is felt to be more
prominent when the skin gets darker. Thus, the amount of fat in
the skin surface feels higher.
The translucent feel is explained by the physical features ofmelanin and hemoglobin amounts. Because coefficients formelanin and hemoglobin are both negative values, thetranslucent feel increases as the amount of pigment decreases.
These results show that skin’s translucent feel is determined by
texture and moisture in each layer and the amount of melaninand hemoglobin. These results are like those of prior research[6], which found that the amount of light absorbed in the skindecreases as the amount of melanin and hemoglobin decreases.
Hence, the translucent feel increases. This suggests validity.
Lastly, the moist feel is explained by the two physicalcharacteristics of shape and the amount of melanin. The fact thatvalues are negative, both for the shape characteristics and themelanin coefficients, as specular reflection gets stronger and theamount of melanin in the skin decreases, the “moist feel” getsstronger. Thus, the skin’s translucent feel is strongly affected by
the rate of light absorption, whereas, for moist feel, the specular

6. MODELING THE RELATION BETWEEN SKIN
IMPRESSION AND PHYSICAL
CHARACTERISTICS
To reveal the relation between physical characteristics andimpression factors obtained in the previous section, we
performed a regression analysis on the amount of melanin, theamount of hemoglobin, and the physical characteristics in thestimuli images used for impression evaluation as predictor
variables and the factor scores of high-order impression factors
as objective variables (2 layer model). Regression analysis was
also performed with the physical characteristics as predictor
variables, scores of low-order impression factors as objective
variables and scores of low-order impression as predictor
variables, scores of high-order impression factors as objective
variables (3 layer model). A prediction model was then built toestimate high-order impression factors through low-orderimpression factors, from the skin’s physical features.

6.1 Method
Using a stepwise method with a reference value, F, a multiple
regression analysis was performed. We inserted variables with F
lower than 0.5. A variance inflation factor of 10 or lower was
indicated for all variables in this analysis. Multi-collinearity
between variables could not be confirmed.
Both “beautiful feel” and “vital feel” can be estimated with a(a) 2 layer model(b) 3 layer model
Figure 4: Estimation of high-order impression through low-order impression from physical characteristics

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reflection component has an impact. Furthermore, based on acomparison of the results for “sticky feel” and “moist feel,” theseare judged to be because there are oily components where theamount of skin pigment is high. Conversely, they can be causedby moisture where the amount of skin pigment is low.
6.2.2 High-order impression. The prediction model for
estimation of high-order impressions, based on low-orderimpressions, revealed that the “beautiful feel” is explained by“sticky feel,” “translucent feel” and “moist feel.” Based on thenegative value for the “sticky” feel coefficient and the positive
values for “beautiful” and “moist” feel coefficients, skin that is
not sticky but is translucent and moist has a strong feel of“beauty.” Because coefficients are higher for translucent feelthan for moist feel, “translucent feel” plays a stronger role in theperception of beauty. Moreover, looking at beauty in terms ofphysical characteristics, values were negative for melanin andhemoglobin coefficients, but positive for the shapecharacteristics coefficient. “Vital feel” is explained by the same
three low-order impressions as “beautiful.” Because values arenegative for the “sticky” and “translucent” coefficients andpositive for “sticky,” skin with a little “sticky” and “translucent”
and a high-level “moist,” had a strong “vital feel.”

ACKNOWLEDGMENTS
Part of the present study was made possible by a JSPS Grant‐in‐
Aid for Scientific Research 15K00387, a Grant-in-Aid for
Scientific Research on Innovative Areas, “Brain and Information
Science on Shitsukan (material perception)” (grant number:
25135732; representative: Noriko Nagata), and by the JST Centerof Innovation Program “Center of Kansei-oriented Digital
Fabrication”.

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7. CONCLUSION
We performed systematic modeling of the relation between thephysical characteristics of skin and the visual impression thatthese provided. Firstly, representative terms having a highdegree of comprehensiveness and representativeness for theevaluation of skin were selected from generally used terms.
Next, CG skin images were created using a technique capable ofaccurately representing skin quality, based on skin’s physicalcharacteristics. Then, using the selected representative terms andthe CG skin images, a subjective evaluation experiment was
performed with male and female subjects in their 20s, revealing
the make-up of skin impression in the age group highlyinterested in skin. Lastly, a regression analysis was performed
with factor scores obtained in the subjective evaluation
experiment as objective variables and physical characteristics aspredictor variables, to build a model to estimate the impression,
based on skin’s physical characteristics. Thus, its statistical
validity was confirmed. The findings were then discussed.
Future issues include the creation of a systematic model that
accounts for pigments other than those of the present study.
Furthermore, this study only dealt with microscale physicalcharacteristics, but because mesoscale physical characteristics,
such as wrinkles and pores, are an important factor in actual
skin, a model needs to be built that includes these characteristics.
Apart from this expansion of physical characteristics, we also
want to conduct a subjective evaluation with subjects from awider range of age groups to ascertain generational differencesin the make-up of skin impression. This requires further study ofmore statistically suitable techniques for evaluation term
recruitment standards.
We further aim to realize a system to generate skin images
with exaggerated impression factors, based on the systematic
model obtained in this study.

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