Nail Detection Model Using User Profile Data
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Solution Overview
Problem
Existing techniques for detecting nail contours using machine learning with images of hands, including nails, face challenges due to variations in nail sizes and shapes caused by environmental, geographical, age, and gender factors, leading to erroneous detections.
Innovation Solution
A method involving an information processing apparatus that acquires image data of a user's finger and user information, using a learning model to detect nail areas by inputting this data and outputting the nail area data for precise nail contour detection, allowing for manual correction and updating of the learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning is used to detect nail contours using only images of hands, then automated detection can be achieved, but detection accuracy deteriorates due to variations in nail sizes and shapes across different users
Solution Approach 1:
The patent transitions from using only image data (2D visual information) to incorporating user information attributes (demographic, environmental, lifestyle factors) as additional dimensions of input data. This multi-dimensional approach allows the learning model to account for variations in nail characteristics across different user groups, improving detection accuracy while maintaining automation.
Solution Approach 2:
The patent changes the parameters input to the learning model from solely image features to include both image features and user information parameters (age, gender, location, lifestyle factors). This parameter expansion enables the model to adapt to diverse nail characteristics and reduce detection errors associated with natural variations in nail size and shape.
2Measurement precision
If learning data is collected from diverse user groups to account for nail variations, then detection accuracy improves, but data collection complexity and time increase
Solution Approach 1:
The patent performs preliminary collection and storage of user information attributes (demographic data, environmental factors, lifestyle information) before the actual nail detection process. This pre-prepared user profile data is then quickly integrated with image data during detection, avoiding time-consuming data collection at the moment of use while still benefiting from comprehensive user characteristics.
Solution Approach 2:
The patent uses pre-collected user information profiles as representative copies of user characteristics. Instead of collecting fresh data for each detection task, the system utilizes stored user attribute profiles that capture essential variations in nail characteristics, enabling rapid and accurate detection without repeated data collection efforts.
3Measurement precision
If user information is integrated into the learning model input, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a universal learning model architecture that can process both image data and user information through a unified processing framework. The same neural network structure handles multiple input types (visual and attribute data), eliminating the need for separate processing systems and reducing overall system complexity despite the enhanced functionality.
Solution Approach 2:
The patent introduces an information processing apparatus as an intermediary that manages the integration of image data and user information. This intermediary component handles data preprocessing, feature extraction, and coordination between different data sources, simplifying the overall system architecture by centralizing the complexity management in a dedicated module rather than distributing it across multiple components.
Data Source
AI summary
A nail application causes an information processing apparatus to function as a user information input unit that acquires user information, an image display control unit that acquires image data of a finger inclusive of a nail of a user, and a nail detection unit that detects a nail area based on data indicating the nail area in the image data to be outputted from a nail detection model as a consequence of inputting the image data and the user information to the nail detection model.