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Human Skin Profiling by Physical Skin Biomarkers: A Machine Learning Approach

  • Davoud Rahimi ArdaliEmail author
  • Lars Rüether
  • Viktor Popov
  • Gerrit Schlippe
  • Branislav Vuksanovic
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)

Abstract

Personalized skincare requires customization of products, which are adapted in a way to suit patient’s skin profile. The process of customization involves several major steps: (i) determine the information which will be used to successfully distinguish between patient’s skin profiles and the methods to obtain the information, (ii) develop an efficient algorithm which will accurately and efficiently classify the patients according to the information used to distinguish between skin profiles, (iii) determine the correct or most efficient skincare formula to suit the particular skin profile. This study considers the first two steps. First, it was examined whether patient’s physical skin measurements can be used to determine the patient’s skin profile. In the second step, twenty machine learning algorithms, which were selected after initial screening, were employed for the classification task. The study showed that the use of physical skin measurements to distinguish between skin profiles represents a promising option. It was also shown that some of the machine learning algorithms are particularly suitable for classification tasks of this type. Sensitivity of the selected classification algorithms to the location on the skin which is sampled, i.e. affected or unaffected part of skin for volunteers with diabetes mellitus type II and rosacea, is reported.

Keywords

Personalized skincare Physical skin biomarkers Classification of skin profiles Machine learning algorithms Skin profiling 

Notes

Acknowledgements

The present study was partially supported by the FP7 EC Programme, Theme FoF.NMP.2013-6 Mini-factories for customized products using local flexible production, Grant agreement no: 609198.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of PortsmouthPortsmouthUK
  2. 2.Dermatest GmbHMünsterGermany
  3. 3.Ascend Technologies Ltd.SouthamptonUK

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