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Estimation of Skin Moisture and Elasticity from Facial Image by using Kernel Ridge Regression

by Motoki Sakai, Yuichi Okuyama
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 3
Year of Publication: 2013
Authors: Motoki Sakai, Yuichi Okuyama
10.5120/12473-8866

Motoki Sakai, Yuichi Okuyama . Estimation of Skin Moisture and Elasticity from Facial Image by using Kernel Ridge Regression. International Journal of Computer Applications. 72, 3 ( June 2013), 12-18. DOI=10.5120/12473-8866

@article{ 10.5120/12473-8866,
author = { Motoki Sakai, Yuichi Okuyama },
title = { Estimation of Skin Moisture and Elasticity from Facial Image by using Kernel Ridge Regression },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 3 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number3/12473-8866/ },
doi = { 10.5120/12473-8866 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:56.128382+05:30
%A Motoki Sakai
%A Yuichi Okuyama
%T Estimation of Skin Moisture and Elasticity from Facial Image by using Kernel Ridge Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 3
%P 12-18
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Various assessment characteristics have been used to evaluate the physiological condition of the skin, including skin moisture, elasticity, oil, and color. This often requires specific pieces of equipment such as a microscope. Although everyday evaluations may be needed to maintain skin condition, a particular piece of equipment may not be suitable for daily use. In this paper, it was proposed that a method to estimate skin moisture and elasticity from a facial image shot by a typical camera. The facial image's RGB, HSV, and YCrCb components were extracted as the explanatory variables for kernel ridge regression (KRR). In general processing, one color space is often adopted for a single purpose. In this research, some of the color components of various color spaces were selectively combined as explanatory variables for KRR. To select suitable explanatory variables, the sequential feature selection (SFS) method was applied. As a result, the correlation coefficient between the estimated and measured skin moisture values was 0. 35. These results showed that skin moisture estimation using the facial image was insufficient. In contrast, the correlation coefficient between the estimated and measured skin elasticity values was 0. 72, indicating that the skin elasticity estimation was successful.

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Index Terms

Computer Science
Information Sciences

Keywords

Skin moisture and elasticity Facial image Kernel ridge regression

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