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Deep-Learning-Based Morphological Feature Segmentation for Facial Skin Image Analysis.

Huisu Yoon1, Semin Kim1, Jongha Lee1

  • 1AI R&D Center, Lululab Inc., 318 Dosan-daero, Gangnam-gu, Seoul 06054, Republic of Korea.

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|June 10, 2023
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Summary
This summary is machine-generated.

This study introduces a novel deep learning method for simultaneously segmenting facial wrinkles and pores. The approach enhances skin analysis by focusing on morphological structures, outperforming existing techniques.

Keywords:
attentionfacial skin feature segmentationfacial wrinkles and poresground truth generationpositional encodingprior informationsemantic segmentation

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Area of Science:

  • Dermatology
  • Computer Vision
  • Medical Imaging

Background:

  • Facial skin analysis is crucial for personalized skincare and cosmetic recommendations in aesthetic dermatology.
  • Current methods often analyze skin features independently, limiting comprehensive analysis.
  • Analyzing morphological structures offers a deeper understanding of skin health beyond color-based assessments.

Purpose of the Study:

  • To develop a deep-learning-based method for simultaneous segmentation of facial wrinkles and pores.
  • To improve the accuracy and efficiency of facial skin analysis by integrating feature processing.
  • To establish a novel approach for facial skin analysis focusing on morphological characteristics.

Main Methods:

  • A U-Net architecture with an encoder-decoder structure was employed for simultaneous segmentation.
  • Two attention schemes were integrated to enhance the model's focus on critical facial areas.
  • A new ground truth generation scheme was developed, tailored to the resolution of both wrinkles and pores.
  • Positional information learning was enhanced, leveraging the fixed locations of skin features.

Main Results:

  • The proposed unified method demonstrated excellent localization accuracy for both wrinkles and pores.
  • The deep learning approach significantly outperformed conventional image-processing methods.
  • The method showed superior performance compared to recent successful deep learning-based approaches.

Conclusions:

  • The developed deep learning model offers a powerful tool for simultaneous wrinkle and pore segmentation in facial skin analysis.
  • This morphological analysis approach advances aesthetic dermatology and opens avenues for future applications.
  • Potential expansions include age estimation and disease prediction based on detailed facial skin analysis.