Skin Tone Analysis Through Skin Tone Map Generation With Optical Approach and Deep Learning.

Background: Skin tone assessment is critical in both cosmetic and medical fields, yet traditional methods like the individual typology angle (ITA) have limitations, such as sensitivity to illuminants and insensitivity to skin redness.

Methods: This study introduces an automated image-based method for skin tone mapping by applying optical approaches and deep learning. The method generates skin tone maps by leveraging the illuminant spectrum, segments the skin region from face images, and identifies the corresponding skin tone on the map.

Deep learning-based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions.

Background: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics.

Aims: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis.

Methods: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images.

Facial wrinkle segmentation using weighted deep supervision and semi-automatic labeling.

Facial wrinkles are important indicators of human aging. Recently, a method using deep learning and a semi-automatic labeling was proposed to segment facial wrinkles, which showed much better performance than conventional image-processing-based methods. However, the difficulty of wrinkle segmentation remains challenging due to the thinness of wrinkles and their small proportion in the entire image.

Generation of skin tone and pigmented region-modified images using a pigment discrimination model trained with an optical approach.

Background: Skin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility.

Deep learning-based pigment analysis model trained with optical approach and ground truth assistance.

This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources.

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

Facial skin analysis has attracted considerable attention in the skin health domain. The results of facial skin analysis can be used to provide skin care and cosmetic recommendations in aesthetic dermatology. Because of the existence of several skin features, grouping similar features and processing them together can improve skin analysis.

Deep learning-based optical approach for skin analysis of melanin and hemoglobin distribution.

Significance: Melanin and hemoglobin have been measured as important diagnostic indicators of facial skin conditions for aesthetic and diagnostic purposes. Commercial clinical equipment provides reliable analysis results, but it has several drawbacks: exclusive to the acquisition system, expensive, and computationally intensive.

Aim: We propose an approach to alleviate those drawbacks using a deep learning model trained to solve the forward problem of light-tissue interactions.

Enhanced electrogenerated chemiluminescence of phenylethynylpyrene derivatives: use of weakly electron-donating group as a substituent.

A weakly donating group (n-propyl) has been used as a substituent at the para-position of the phenyl group for a series of phenylethynylpyrene derivatives where the number of phenylethynyl peripheral arms appended to the pyrene core is varied. This system markedly improved the concurrent stability of both cation and anion radicals and consequently greatly improved electrogenerated chemiluminescence (ECL). Density functional theory (DFT)-based theoretical calculations supported the associated photophysical and electrochemical properties of the series compounds.