Deep-learning-guided independent component analysis for characterizing facial skin melanin and hemoglobin distribution

Huisu Yoon; Geunho Jung; Semin Kim; Chanhyeok Lee; Sangwook Yoo; Jongha Lee

doi:10.1117/12.3002337

12 March 2024 Deep-learning-guided independent component analysis for characterizing facial skin melanin and hemoglobin distribution

Huisu Yoon, Geunho Jung, Semin Kim, Chanhyeok Lee, Sangwook Yoo, Jongha Lee

Author Affiliations +

Proceedings Volume 12845, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2024; 1284504 (2024) https://doi.org/10.1117/12.3002337
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

One common approach to separatingmelanin and hemoglobin distribution from a color image is Independent Component Analysis (ICA). In this study, we propose a method based on deep learning to automatically detect suitable areas for successful facial pigmentation analysis. To do that, three deep learning models are utilized for segmentation and localization to offer a candidate region for ICA. The experiment was conducted using cross-polarized facial images selected from 200 subjects, and results showed that the deep learning-guided ICA can effectively identify regions of hyperpigmentation and successfully separate melanin and hemoglobin maps for evaluation.

Conference Presentation

Citation Download Citation

Huisu Yoon, Geunho Jung, Semin Kim, Chanhyeok Lee, Sangwook Yoo, and Jongha Lee "Deep-learning-guided independent component analysis for characterizing facial skin melanin and hemoglobin distribution", Proc. SPIE 12845, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2024, 1284504 (12 March 2024); https://doi.org/10.1117/12.3002337

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