ArticleJournal of cosmetic dermatology2025
Applicability of Complementary Colors in Skin Tone Correction for Young Chinese Adults Based on Image Processing and Machine Learning.
Article in Journal of cosmetic dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Applicability of Complementary Colors in Skin Tone Correction for Young Chinese Adults Based on Image Processing and Machine Learning.Journal of cosmetic dermatology · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveSkin tone correction is an essential focus within dermatology and cosmetology, particularly in achieving a balanced and even facial appearance. The application of complementary color theory in skin tone correction remains predominantly subjective, relying on individual user experiences rather than systematic and quantitative assessments. This study aims to evaluate the applicability of complementary color theory among young Chinese individuals and develop predictive models for personalized skin tone correction.
methodsSixteen young Chinese female participants aged 20-25 were recruited. Standardized facial images were captured using the VISIA-CR system under standardized lighting conditions, both before and after the application of six color-correcting primers (orange, pink, blue, white, purple, and green). Four facial regions of interest (ROIs), defined as the forehead, under-eye circles, cheeks, and near-nose, were analyzed. Five colorimetric indices (L*, a*, b*, ITA°, and Hab°) were quantified across each ROI. State-of-the-art machine learning regression models were developed to predict post-application ITA° and Hab° values based on pre-application skin tone and primer characteristics.
resultsUnder-eye circles exhibited the darkest and most yellowish-red skin tone compared to other regions. Complementary color primers demonstrated statistically significant improvements in ITA° and Hab° values across all ROIs. Pink primers were most effective for under-eye dark circles, while purple, pink, and blue primers resulted in greater improvements in overall skin tone. LightGBM and XGBoost regression models demonstrated superior performance, with R
conclusionThis study robustly validates the efficacy of complementary color primers in skin tone correction among young Chinese individuals. The integration of machine learning offers a robust framework for personalized cosmetic recommendations, paving the way for innovative and data-driven advancements in skincare and makeup applications.
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