Makeup Color Prediction Model Using Intermediary Calibration
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Solution Overview
Problem
Selecting the right makeup color online is challenging due to inaccuracies in color representation caused by screen brightness, camera conditions, and lighting, as well as changes in makeup color when applied, which existing color correction algorithms struggle to address effectively.
Innovation Solution
A system and method for generating a prediction model that uses a data storage device and modeling engine to create a color matching model based on skin and makeup color sets, employing image processing and machine learning techniques to account for variations in lighting and camera dependencies, and calibrating images using a color checker for accurate color conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color correction algorithms are used to estimate and remove illumination influence, then color accuracy is improved, but the algorithms strongly rely on assumptions that may not hold, leading to poor performance
Solution Approach 1:
The patent introduces an intermediary calibration target with known color values that mediates between the camera system and the color measurement. This calibration target serves as a reference that allows the system to learn the camera's color response characteristics without relying on assumptions about illumination or scene content, thereby resolving the contradiction between color accuracy and algorithm reliability
Solution Approach 2:
The patent performs preliminary calibration by capturing images of a calibration target with known color values before actual color measurement. This preliminary action establishes a mapping between device-dependent RGB values and standard color values, enabling accurate color measurement without relying on assumptions during the actual measurement process
2Ease of operation
If device-dependent RGB values are directly used for color matching, then the process is simple, but the colors look significantly different from actual colors due to automatic image processing
Solution Approach 1:
The patent uses a calibration target as an intermediary to transform device-dependent RGB values into standard color values. This intermediary provides a reference framework that enables accurate color representation while maintaining a relatively simple process through automated calibration and lookup table-based color conversion
Solution Approach 2:
The patent creates a copy of the standard color space mapping through calibration, storing the relationship between device-dependent and standard color values in lookup tables. This copying approach allows accurate color conversion without complex real-time processing, balancing simplicity and accuracy
3Loss of information
If color constancy algorithms are used to estimate scene illumination, then color information can be extracted, but the algorithms strongly rely on assumptions that may not hold in various conditions
Solution Approach 1:
The patent performs preliminary calibration by capturing images of a calibration target under various lighting conditions before actual color measurement. This preliminary action allows the system to learn the camera's illumination-dependent color response and compensate for it, enabling reliable color information extraction without relying on assumptions about scene illumination during measurement
Data Source
AI summary
A system generates a prediction model for makeup color matching. The system includes a data storage device and a modeling engine. The data storage device stores a plurality of color sets comprising a skin color set, a makeup color set, and a target color set. The modeling engine is coupled to the data storage device and configured to generate a prediction model and an output color set. The prediction model models generation of the target color set based on inputs from the skin color set and the makeup color set. The output color set approximates the target color set with a predictable accuracy.