AI Skin Image Analysis for Personalized Body Contour Matching
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Solution Overview
Problem
Existing cosmeceutical and grooming products lack personalized feedback or guidance to address individual body contours, leading to varying effectiveness due to different chemical compositions and formulations, which are not tailored to a user's specific body contour characteristics.
Innovation Solution
A digital imaging system and method using artificial intelligence to analyze pixel data of a user's skin area, training a body contour model with thousands of images to generate user-specific recommendations for addressing body contour features, which can be rendered on a display screen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cosmeceutical and grooming products are formulated for different body contour types, then product effectiveness for specific body contours is improved, but product complexity and variety increase
Solution Approach 1:
The system captures images of the user's body contour, analyzes them using AI/ML algorithms, and provides feedback about the detected contour type. This feedback loop enables personalized product recommendations matched to the user's specific body contour, improving effectiveness without requiring the user to manually select from complex product categories.
Solution Approach 2:
The system automatically detects and classifies body contour types through image analysis without requiring user input or manual assessment. The AI model processes images and generates contour type classifications autonomously, eliminating the need for users to understand complex product formulations or manually match products to their body types.
2Measurement precision
If AI/ML models are trained with large datasets of pixel data, then body contour detection accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The AI/ML models are pre-trained offline on large datasets of pixel data from numerous images. This preliminary training phase prepares the models in advance, so that during actual use, the pre-trained models can quickly and accurately classify body contours with minimal computational overhead, reducing real-time energy consumption while maintaining high accuracy.
3Adaptability or versatility
If user-specific recommendations are generated based on pixel data analysis, then personalized product matching is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system introduces an intermediary AI/ML-based body contour detection service that bridges the gap between raw pixel data and product recommendations. This intermediary service handles the complex data processing, analysis, and pattern recognition tasks, converting complex image data into simplified contour type classifications that can then be easily mapped to appropriate product recommendations.
Data Source
AI summary
Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining body contour. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A body contour model, trained with the pixel data, is operable to output, across a range of a body contour scale, body contour values associated with a degree of body contour. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the body contour model, to determine a user-specific body contour value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.