Applicability of Complementary Colors in Skin Tone Correction for Young Chinese Adults Based on Image Processing and...

Area of Science:

• Dermatology and Cosmetology
• Color Science
• Machine Learning

Background:

• Skin tone correction is crucial for balanced facial appearance.
• Current methods rely on subjective complementary color theory.
• Quantitative assessment is needed for personalized skin tone correction.

Purpose of the Study:

• Evaluate complementary color theory for young Chinese individuals.
• Develop predictive models for personalized skin tone correction.
• Quantify skin tone improvements using colorimetric indices.

Main Methods:

• Recruited 16 young Chinese females (aged 20-25).
• Captured standardized facial images using VISIA-CR.
• Analyzed five colorimetric indices (L*, a*, b*, ITA°, Hab°) in four facial regions.
• Developed machine learning models to predict post-application skin tone.

Main Results:

• Under-eye circles were darkest and most yellowish-red.
• Complementary color primers significantly improved ITA° and Hab° values.
• Pink primers were best for under-eye circles; purple, pink, and blue improved overall tone.
• Machine learning models achieved R² of 0.824 for ITA° and 0.850 for Hab°.

Conclusions:

• Efficacy of complementary color primers is validated for young Chinese individuals.
• Machine learning provides a framework for personalized cosmetic recommendations.
• Data-driven approaches can advance skincare and makeup applications.