Attribute Factor Analysis for Facial Image Regions
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Solution Overview
Problem
Current supervised learning methods, such as those used in facial image recognition, cannot analyze or visualize which facial parts are decisive factors for attributes like sex or prettiness, as they lack technology to determine the overall tendency of feature variables influencing attribute results.
Innovation Solution
An attribute factor analysis method that divides image data into mesh-shaped parts, reconstructs sample sets, analyzes dependencies between feature values and attribute data, and visualizes the results to identify decisive facial regions for specific attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used to estimate attributes from image data, then attribute estimation accuracy is improved, but the ability to analyze and visualize which image regions are decisive factors for the attribute is lost
Solution Approach 1:
The image data is divided into multiple regions of interest (ROIs) through mesh-shaped division. This segmentation allows the system to analyze which specific regions contribute to attribute estimation while maintaining overall estimation accuracy. The division unit separates the image into manageable parts that can be independently evaluated for their contribution to the final attribute prediction.
Solution Approach 2:
A reconstruction unit acts as an intermediary between the original image data and the attribute estimation process. It reconstructs image data with modified ROI characteristics (such as replacing facial features with neutral patterns) to determine which regions are decisive factors. This intermediary process enables the system to identify important regions without compromising the overall attribute estimation capability.
2Adaptability or versatility
If the entire image is used for attribute estimation, then comprehensive attribute prediction is achieved, but identification of specific decisive image regions becomes difficult
Solution Approach 1:
The image is divided into multiple ROIs using a mesh-shaped division pattern. This segmentation enables the system to maintain comprehensive attribute prediction while simultaneously identifying which specific regions are decisive factors. Each ROI can be independently analyzed to determine its contribution to the overall attribute estimation.
Solution Approach 2:
The system applies different analysis treatments to different regions of the image. By modifying specific ROI characteristics (such as replacing facial features with neutral patterns) while keeping other regions unchanged, the system can identify which local regions are decisive factors for attribute estimation, thus achieving both comprehensive prediction and region-specific identification.
3Manufacturing precision
If image data is divided into small mesh parts, then decisive regions can be precisely identified, but the sample size for each region becomes insufficient for reliable analysis
Solution Approach 1:
Multiple image data samples are combined and processed together to ensure sufficient sample size for each ROI. The system aggregates information across multiple samples while maintaining the mesh-shaped division structure, thus achieving both precise region identification and reliable statistical analysis with adequate sample sizes.
Solution Approach 2:
The system transitions from analyzing single images in isolation to analyzing multiple images simultaneously in a multi-dimensional space. By processing a plurality of image data samples together, the system maintains sufficient sample size for each ROI while preserving the precision of region identification through the mesh-shaped division approach.
Data Source
AI summary
This invention relates to a method of analyzing a factor of an attribute based on a case sample set containing combinations of image data and attribute data associated with the image data. The attribute factor analysis method includes: a division step of dividing an image region of the image data forming each element of the case sample set into parts in a mesh shape of a predetermined sample size; a reconstruction step of reconstructing, based on the case sample set, the case sample sets for the respective parts to obtain reconstructed case sample sets; an analysis step of analyzing, for each of the reconstructed case sample sets, a dependency between an explanatory variable representing a feature value of image data on each part and an objective variable representing the attribute data, to thereby obtain an attribute factor analysis result; and a visualization step of visualizing the attribute factor analysis result to produce the visualized attribute factor analysis result.