Lip-Makeup Identification via Mouth Image Color Matching
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Solution Overview
Problem
Existing methods fail to accurately identify and recommend lip-makeup products based on images due to differences in color and texture between the applied product and the bulk or packaging, making it difficult for customers to find matching products.
Innovation Solution
A method and system that detect the mouth in an input image, determine if it's made-up, compute color and texture parameters, and match them with reference products from a database, ensuring the selected product is a close and relevant match by using trained models and k-means algorithms for color parameter determination and machine learning for texture analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If color parameters are extracted from bulk product or packaging, then product identification is simplified, but the identified color does not match the actual appearance on lips
Solution Approach 1:
The patent uses image processing to create a digital copy of the lip-makeup product as it actually appears on the lips. By extracting color parameters from the applied product in the image rather than from bulk product or packaging, the system creates an accurate visual representation that matches the real-world appearance, resolving the contradiction between identification ease and color accuracy
Solution Approach 2:
The patent transforms the identification approach by changing from extracting color parameters from physical bulk product to extracting them from digital image data of the applied product. This parameter change enables accurate color matching by using the actual visual appearance on lips as the reference, rather than relying on bulk product colors that may differ
2Speed
If a simple color comparison method is used, then the processing speed is fast, but the accuracy of matching the correct lip-makeup product is insufficient
Solution Approach 1:
The patent segments the lip-makeup product identification process into multiple independent steps: mouth detection, made-up status determination, color parameter extraction, texture analysis, and distance computation. This segmentation allows each step to be optimized independently, maintaining processing speed while improving overall matching accuracy through comprehensive analysis
Solution Approach 2:
The patent extends the matching criteria from a single color dimension to multiple dimensions including color parameters (multiple values) and texture characteristics. By adding these additional dimensions, the system achieves more accurate product matching while maintaining efficiency through systematic processing of each dimension
3Measurement precision
If multiple parameters including texture are analyzed, then product matching accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent divides the complex analysis into separate modular components: mouth detection module, color parameter extraction module, texture analysis module, and matching computation module. Each module handles a specific aspect of the analysis independently, which improves accuracy through comprehensive parameter analysis while managing system complexity through modular design
Solution Approach 2:
The patent creates a multi-functional system that can detect mouth presence, determine made-up status, extract color parameters, analyze texture, and compute matching distances all within a single integrated framework. This universal approach handles multiple analysis functions in a coordinated manner, improving accuracy without proportionally increasing complexity
4Productivity
If a threshold-based filtering is applied, then irrelevant products are eliminated, but some potentially matching products may be excluded
Solution Approach 1:
The patent uses a distance threshold parameter to control the filtering behavior. By adjusting this parameter, the system can balance between efficiency (higher threshold excludes more irrelevant products) and coverage (lower threshold includes more potential matches). This parameter-based control allows flexible adaptation to different requirements without changing the underlying system structure
Data Source
AI summary
Disclosed is a method for identifying a lip-makeup product from an image, the method including: detecting a mouth on the image; determining whether the mouth is made-up or not; and if the mouth of the person is made-up: determining color parameters of the lip-makeup of the image from at least some of the pixels of the image belonging to the mouth; computing a distance between the color parameters of the lip-makeup from the image and the color parameters of at least some of a plurality of reference lip-makeup products; and if at least one distance between the color parameters of the lip-makeup of the input image and one of the reference lip-makeup product is below a predetermined threshold, identifying a reference lip-makeup product best matching the lip-makeup product on the image.