Digital Image Analysis for Under-Eye Skin Color Quantification
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Solution Overview
Problem
Current methods for assessing under eye dark circles are subjective, prone to variation due to skin contact during instrumental measurements, and lack specificity and automation in image analysis, making them inaccurate and unreliable for quantitative evaluation.
Innovation Solution
A method using digital image analysis that involves color correction, automatic detection and alignment of eye features, calculation of skin reflective intensity profiles, and comparison of pre- and post-treatment profiles to quantify changes in under eye skin appearance, employing computer algorithms and digital photography.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual grading by trained clinical grader is used, then subjective assessment can be performed, but accuracy and reproducibility deteriorate
Solution Approach 1:
The patent replaces the manual visual grading system with an automated image analysis system using computer algorithms. The system captures facial images, automatically detects eye area features, aligns images, and quantifies dark circle severity through pixel intensity analysis, eliminating subjective human assessment while maintaining ease of operation.
Solution Approach 2:
The image analysis system performs self-alignment and self-calibration by automatically detecting eye features (eyelids, eyeballs, eyebrows) and adjusting image orientation and positioning. This self-service capability ensures consistent measurement without requiring manual intervention, improving both accuracy and reproducibility.
2Reliability
If instrumental colorimetric measurement is used, then objective measurement can be obtained, but measurement precision deteriorates due to skin contact and limited accessibility
Solution Approach 1:
The patent uses digital image capture to create a visual copy of the eye area, which is then analyzed through image processing algorithms. This copying approach allows objective measurement without physical contact with the skin, eliminating the variability introduced by instrument-skin contact while maintaining measurement precision through pixel-level analysis.
Solution Approach 2:
The patent introduces digital imaging as an intermediary between the measurement system and the skin. Instead of direct contact measurement, the system captures light reflected from the skin surface and analyzes it computationally, eliminating the harmful effect of physical contact while preserving objective measurement capabilities.
3Extent of automation
If general image analysis method is used, then automation can be achieved, but measurement precision deteriorates due to lack of specificity and alignment
Solution Approach 1:
The patent applies local quality by specifically targeting the eye area for analysis rather than treating the entire face uniformly. The system detects and isolates eye features (upper and lower eyelids, eyeballs, eyebrows) and focuses measurement on the under-eye region, improving precision by concentrating analysis on the relevant local area with appropriate geometric constraints.
Solution Approach 2:
The patent performs preliminary alignment and positioning actions by automatically detecting eye features and rotating/positioning the image to proper orientation before measurement. This preliminary action ensures that subsequent measurements are taken from correctly aligned images, eliminating variability caused by misalignment and improving measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides an objective, accurate, and reproducible method for evaluating under eye dark circles, reducing subjectivity and variability, and effectively quantifying the efficacy of skin care products by analyzing skin reflective intensity profiles.
Implementation Method 1
The first step is to take a digital photograph... using any commercially available digital camera
Implementation Method 2
The color correction process is then carried out using a set of computer algorithms
Implementation Method 3
calculating skin reflective intensity in a fashion which scans across the region of interest stepwise from the upper eyelid area down to the under eye area
Data Source
AI summary
Methods for quantitative measurement of under eye dark circles and other color related phenomena in eye area skin are described. The methods can be used to quantify the clinical efficacy of skin care products.