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Development of the multispectral UV polarization reflectance imaging system (MUPRIS) for in situ monitoring of the UV protection efficacy of sunscreen on human skin.

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Skin patch based makeup finish assessment technique by deep neural network.

Ken Nishino1

  • 1Makeup Products Research, Kao Corporation, Odawara, Kanagawa, Japan.

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)
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Summary
This summary is machine-generated.

Deep neural networks (DNNs) analyzed cosmetic skin textures using skin patches. This machine learning approach accurately evaluates makeup finish, aiding visual science and cosmetics development.

Keywords:
CNNDNNmakeupskin image analysisskin patch

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Area of Science:

  • Computer Vision
  • Cosmetic Science
  • Machine Learning

Background:

  • Skin appearance significantly influences perceptions.
  • Objective evaluation of makeup finish is crucial for product development.
  • Machine learning, specifically deep neural networks (DNNs), offers potential for analyzing complex skin textures.

Purpose of the Study:

  • To apply DNNs for accurate analysis and evaluation of cosmetic skin textures.
  • To develop objective methods for assessing makeup finish.
  • To explore the utility of skin patch datasets in DNN training for cosmetic applications.

Main Methods:

  • Extracted skin patch datasets from facial images for DNN model training.
  • Trained DNNs using classification (skin attributes) and regression (expert visual assessment) tasks.
  • Utilized skin patches to retain texture detail and enable facial visualization.

Main Results:

  • Developed skin patch-based classifiers for age, makeup presence, formulation type, and application timing.
  • Achieved high prediction accuracy in the regression task for expert visual assessment.
  • Demonstrated effective evaluation of actual makeup conditions aligned with visual appearance.

Conclusions:

  • DNNs trained on skin patches provide an effective method for evaluating makeup finish.
  • This approach has potential applications in visual science and cosmetics R&D.
  • Future research could focus on diverse skin conditions and personalized cosmetics.