Facial Type Diagnosis Using Multi-Expression Feature Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing facial-type diagnostic systems fail to accurately diagnose facial types based on comprehensive facial impressions due to changes in facial expressions, as they rely on specific feature points from particular expressions, leading to deviations between diagnosed results and overall facial impressions.
Innovation Solution
A facial-type diagnostic apparatus and method that acquires and compares feature values from multiple facial expressions, such as neutral and smiling faces, using a combination of image acquisition, feature extraction, and weight-based calculations to determine facial types, considering the influence of expression changes on facial parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If facial type diagnosis is based on feature points from a particular facial expression, then the diagnosis process is simple and quick, but the diagnosis accuracy deteriorates because the facial impression changes with expression
Solution Approach 1:
The patent combines multiple facial images with different expressions into a single diagnosis process. The system extracts feature points from both a first facial expression image and a second facial expression image, then integrates these features to determine facial type. This merging of multiple expression-based measurements resolves the contradiction by maintaining diagnostic comprehensiveness while preserving processing efficiency through unified analysis framework.
Solution Approach 2:
The patent transitions from single-expression diagnosis to multi-expression diagnosis by adding the dimension of expression variation. Instead of diagnosing based on one facial state, the system incorporates features from multiple expressions, effectively moving the diagnosis into a higher-dimensional feature space that captures both facial structure and expression dynamics, thereby improving accuracy without significantly increasing complexity.
2Device complexity
If only one facial expression is used for diagnosis, then the device complexity is low, but the comprehensive facial impression cannot be accurately reflected
Solution Approach 1:
The system merges multiple facial expression images into a unified diagnosis process. By combining feature extraction from both the first and second facial expressions and integrating these features in the determination unit, the system achieves reliable facial impression assessment without requiring overly complex device architecture. The unified processing framework maintains manageability while improving reliability.
3Measurement precision
If multiple facial expressions are used for diagnosis, then the facial type diagnosis accuracy is improved, but the image acquisition time increases
Solution Approach 1:
The system performs preliminary feature extraction and processing on multiple facial expression images in parallel or pre-computed manner. By preparing the feature data beforehand and organizing it for efficient integration, the system reduces the overall time required for diagnosis while maintaining the benefit of multi-expression analysis. The preliminary action approach allows accurate diagnosis without significant time penalty.
Data Source
AI summary
This facial type diagnostic device has: an image acquisition unit for acquiring a first image capturing a first expression of a user, and a second image capturing a second expression of the user, a feature amount extraction unit for extracting a first feature amount of a facial part of the user in the first image, and a second feature amount of the facial part of the user in the second image; and a facial type determination unit for determining the facial type of the user on the basis of the first feature amount and the second feature amount.