Skin Redness Analysis Model for Personalized Hair Removal Feedback
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Solution Overview
Problem
Individuals are not aware of their specific skin redness levels after hair removal, making it difficult to choose effective hair removal methods and products, as existing systems lack personalized feedback.
Innovation Solution
A digital imaging system and method that analyzes pixel data from user images using a trained skin redness model to determine user-specific skin redness values and provide personalized recommendations for reducing skin redness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generalized hair removal recommendations are provided to users, then users can access hair removal guidance, but users cannot receive personalized feedback suited for their specific skin redness conditions
Solution Approach 1:
The patent implements a feedback mechanism by capturing images of user skin after hair removal, analyzing pixel data to determine skin redness values, and providing personalized recommendations based on the analyzed results. This closed-loop feedback system enables adaptability to individual user conditions while preserving user-specific information through image analysis.
2Measurement precision
If skin redness analysis is implemented using digital imaging, then personalized feedback can be provided, but system complexity increases
Solution Approach 1:
The system enables self-service by using the user's own mobile device camera to capture skin images, eliminating the need for specialized imaging equipment. The processing leverages the device's existing computational capabilities and trained machine learning models to perform skin redness analysis, thereby achieving precise measurement without proportionally increasing device complexity.
Solution Approach 2:
The patent replaces complex mechanical skin analysis equipment with digital imaging and computational algorithms. By using camera-based pixel data analysis and machine learning models to determine skin redness values, the system achieves precise measurement while reducing hardware complexity compared to traditional mechanical or optical measurement devices.
3Loss of information
If users attempt to determine effective hair removal methods based on generalized recommendations, then users can access guidance, but users lack awareness of their specific skin redness levels and reactions
Solution Approach 1:
The system addresses the information gap by providing automated feedback through image analysis. Users simply capture a skin image after hair removal, and the system automatically determines skin redness values and provides personalized recommendations, eliminating the need for users to manually assess their skin conditions while restoring awareness of specific redness levels.
Data Source
AI summary
Digital imaging systems and methods are described for determining a user-specific skin redness value of a user's skin after removing hair. An example method may be performed by one or more processors and may include aggregating training images comprising pixel data of skin of individuals after removing hair. A skin redness model may be trained using the training images to output skin redness values associated with a degree of skin redness from least to most red. The method may include receiving an image of a user including pixel data of the user's skin after hair is removed from the skin, analyzing the image using the skin redness model to determine a user-specific skin redness value, generating a user-specific recommendation designed to address a feature identifiable within the pixel data of the user's skin, and rendering the recommendation on a display screen of a user computing device.