An Evaluation of the Relationship between Impression and the Physical
Properties of Human Skin
Tatsuya Matsumoto
Kensuke Tobitani
Yusuke Tani
Kwansei Gakuin UniversityKwansei Gakuin UniversityKwansei Gakuin University
Hiroki Fujii
Noriko Nagata∗
Kwansei Gakuin University
Kwansei Gakuin University
Keywords: human skin, impression, physical properties
Concepts: •Human-centered computing → Interaction design
theory, concepts and paradigms;

1Introduction

The visual expression of a surface quality of human skin is required
in a wide range of fields, such as in cosmetics industry. It is muchharder, however, to generate intuitively and accurately a computer
graphics (CG) image of human skin that has a desired impression
without professional knowledge and skills because of its complex
physical properties. It is necessary to model the linkage between thevisual impressions and physical properties of human skin in order toeffectively determine parameters for the generation of CG images.

quantities. In this model, bare skin consists of 5 layers. The parameters such as the scattering coefficient and the absorption coefficient, which are required to render a human skin image, were determined from the proportion of eumelanin and pheomelanin in themelanin, the proportion of oxyhemoglobin and deoxyhemoglobinin the hemoglobin, the melanin, the hemoglobin, β-carotene and thequantity of water in each layer. Subsequently, we accurately rendered human skin images based on these parameters by the Monte
Carlo simulation. Finally, we obtained various human skin imagesby changing the quantity of 7 coloring matters except β-carotene
and the quantity of water. Moreover, this model can express achange in the surface quality of human skin caused by the changesof thickness and fine surface roughness due to aging.

2.2 Generating visual stimuli

In this research, we focus on the physical properties of bare skin,clarify impression factors of bare skin and formulate the relationship between these impression factors and physical quantities ofbare skin. This will enable us to estimate an impression from ahuman skin image and derive physical quantities for the accurate
generation of human skin images that have desired impressions.

We controlled 3 typical physical quantities of human skin, theamount of melanin, the amount of hemoglobin and “configurationcharacteristics,” which include skin thickness and the height of finesurface roughness, as parameters for generating human skin images. These physical quantities cause a stronger visual influence onan impression of bare skin than other physical quantities. Table 1
shows the levels of each physical quantity of human skin.

2

We rendered 75 human skin images with varying parameters using PBRT [Pharr et al. 2010] as a physically-based renderer. The
rendering results of melanin 9% are shown in Figure 1. In rendering, we utilized the same 3D model and texture in order to remove
influences caused by caused by parameters other than these three.
Moreover, we rotated viewpoints at 5 degree intervals and rendered
5 images in order to express differences due to a viewpoint (Figure2). Finally, we trimmed away everything except cheek positions of
the human skin images, and adopted these images as visual stimuli for a subjective evaluation experiment. Figure 3 shows humanskin images in each amount of melanin and hemoglobin when theconfiguration characteristic were 30 and 80.

Generating a human skin image based on
physical quantities

In order to model the relationship between an impression and aphysical quantities of human skin, it is necessary to clarify the impressions of human skin. Here, we generated CG images controlled
by typical physical quantities mentioned later as visual stimuli in a
subjective evaluation experiment for investigating impression factors.

2.1

Biophysically-Based Model

We used biophysical quantities of human skin as parameters for thegeneration of CG images. Stephen, Ian D. et al. reported that theblood circulation, melanin and β-carotene influence “healthy exterior”[Stephen et al. 2011]. It is reasonable, therefore, to support
that biophysical quantities of human skin can be linked to impression factors.
We adopted the Biophysically-Based Model[Iglesias-Guitian et al.
2015] to render human skin images accurately based on physical∗ e-mail:nagata@kwansei.ac.jp
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SIGGRAPH ’16, July 24-28, 2016, Anaheim, CA,
ISBN: 978-1-4503-4371-8/16/07
DOI: http://dx.doi.org/10.1145/2945078.2945167

Table 1: Parameters
Physical property

Set valuemelaninpheomelanin:94%, eumelanin:6%
hemoglobin
oxyhemoglobin:64%, deoxyhemoglobin:36%
bilirubin
configuration characteristic0 ˜ 36% (by 9%)
0.1 ˜ 20.1% (by 5%)

Figure 1: Rendering result0.014%
30, 55, 80

3-layered model that enables us to estimate the high-order impressions through the low-order impressions from the physical properties. The model is shown in Figure 4.
Figure 2: Human skin images with rotating viewpoint

Figure 3: Human skin images were rendered in each amount ofmelanin, hemoglobin and (a) configuration characteristic at age
30, (b) configuration characteristic at age 80.

3

Subjective evaluation experiment

We conducted a subjective evaluation experiment to investigate
the impression factors of bare skin. Prior to this experiment,
we collected 21 adjectives for low-order impression and 15 adjectives for high-order impression, which describe the conditionof bare skin appropriately as an adjective scale. Adjectives for
low-order impression include “moist”, “soft”, “resilient”, “glossy”,“shiny”, “translucency”, “dry”, “color evenness”, “white”, “oily”,“sticky”, “fatty”, “arid”, “less moist”, “desiccated”, “muddy”,“dull”, “color unevenness”, “bloated”, “chunky” and “yellow”.
Adjectives for high-order impression include “ruddy”, “lively”,“youthful”, “pure”, “beautiful”, “neat”, “simple”, “natural”, “general”, “subdued”, “clam”, “mature”, “tired”, “unhealthy” and“unattractive”. These adjectives are the Japanese language.We rendered 75 human skin images mentioned in Section 2 as visual stimuli.

3.1

MethodThe subjects of this experiment were 10 Japanese college students
in their 20s (6 men; 4 women; average=22.5; SD=0.67). They evaluated 36 adjective scales about 75 human skin images in 7 stages.

3.2

Results and discussion

We performed a factor analysis for rating the data using the maximum likelihood method and the Promax rotation. As a result of thefactor analysis, we extracted 3 factors based on the Kaiser-Guttmancriterion. In low-order impressions, we determined that Factor 1 is“sticky,” given that the factor loadings for “oily” and “sticky” werehigh. Factor 2 is “translucent,” given that the factor loadings for“white” and “translucency” were high. Factor 3 is “moist,” given
the factor loadings for “moist” and “glossy.” In high-order impressions, we determined that Factor 1 is “beautiful,” given that thefactor loadings for “neat” and “beautiful” were high. Factor 2 is“vital,” given that the factor loadings for “ruddy” and “lively” werehigh.

4

As shown in Figure 4, it was revealed that the “transparency” wascontributed by melanin and hemoglobin. This result supports theresults in a previous work[Masuda et al. 2005], which reported
that the amount of light absorption diminishes and the transparent
increases with decreasing melanin or hemoglobin. “Sticky” and“moist” are similar impressions. However, “sticky” has a positive
correlation with the amount of melanin, and “moist” has a negative correlation with the amount of melanin. Moreover, “sticky” isinfluenced by the amount of hemoglobin. It means that “sticky”
increases when human skin turns pinker or darker, and “moist” increases when human skin becomes fairer. In addition, “sticky” hasa contrary correlation with “moist” to high-order impression. That
is to say, “sticky” and “moist” are quite different impressions.

Modeling the relationship between visual
impressions and physical properties

We developed a predictive model to associate the impression factorsin low and high-order and physical quantities. We constructed theFigure 4: 3-layered model

5 Conclusion
In this work, we modeled and evaluated the relationship between
the impression factors and physical properties of human skin. First,
we generated human skin CG images controlled by typical physicalquantities. Next, we conducted a subjective evaluation experimentusing collected adjective words and generated human skin images
to investigate the impression factors of bare skin. Finally, we associated the impression factors in low and high-order and physicalquantities of bare skin. As a result, we confirmed detailed differences of sticky and moist, which are unknown to experts in thisfield. The developed model enables us to estimate impression factors from a human skin image, and derive physical quantities forthe accurate generation of a human skin image that has a desiredimpression.

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