Biological Information Processor Feature Extraction Precision
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Solution Overview
Problem
Existing biological information processing systems face challenges in accurately detecting and correcting feature points from image data, leading to potential errors in authentication precision due to misalignment and improper detection quality.
Innovation Solution
A biological information processor is designed with an area detection unit, feature extraction unit, and determination unit to assess and correct feature information extraction precision, utilizing a similarity determination unit to compare extracted features with stored data and automatically correct errors, thereby enhancing matching precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feature point detection is performed to enable authentication, then authentication functionality is achieved, but detection errors occur reducing matching precision
Solution Approach 1:
The system calculates a quality indicator for detected feature points and uses this feedback to determine whether to proceed with authentication. If the quality indicator falls below a threshold, the system requests re-detection or rejects the authentication attempt, thereby improving reliability by preventing erroneous authentication based on poor-quality feature points.
Solution Approach 2:
The system performs quality assessment of feature points before proceeding with authentication matching. By evaluating the quality indicator in advance and comparing it against thresholds, the system prevents subsequent authentication errors, ensuring that only high-quality feature points are used for matching.
2Measurement precision
If feature extraction precision is improved through advanced techniques, then extraction accuracy increases, but detection errors still occur requiring quality verification
Solution Approach 1:
The system calculates a quality indicator that provides feedback on the reliability of extracted feature points. This quality metric enables the system to verify detection quality without requiring overly complex verification mechanisms, balancing precision improvement with manageable system complexity.
Solution Approach 2:
The system replaces complex manual verification processes with an automated quality indicator calculation based on mathematical operations on feature point coordinates. This substitution maintains high extraction precision while avoiding the need for cumbersome verification procedures.
3Measurement precision
If manual verification of feature points is performed to ensure quality, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system replaces time-consuming manual verification with automated quality indicator calculation using mathematical operations on feature point coordinates. This substitution maintains verification accuracy while dramatically reducing processing time by eliminating manual intervention.
Solution Approach 2:
The system performs self-verification by automatically calculating quality indicators and making determination decisions without requiring manual inspection. This self-service approach maintains verification accuracy while eliminating the time loss associated with human review.
4Productivity
If feature points are detected from image data for matching, then authentication functionality is enabled, but errors in feature point detection reduce matching precision
Solution Approach 1:
The system performs preliminary quality assessment of feature points before proceeding to matching operations. By calculating quality indicators and comparing them against thresholds in advance, the system ensures that only high-quality feature points are used for matching, thereby maintaining matching precision while enabling efficient automated processing.
Solution Approach 2:
The system uses quality indicator feedback to control the authentication flow. When quality metrics meet thresholds, the system proceeds with matching; when they fall below thresholds, the system requests re-detection or rejection. This feedback mechanism maintains matching precision while preserving processing efficiency through automated decision-making.
Data Source
AI summary
According to one embodiment, a biological information processor includes: an area detection unit configured to detect an area in which a person is displayed from image information; a feature extraction unit configured to extract feature information based on a characteristic portion of a person from the area detected by the area detection unit from the image information; and a determination unit configured to determine an extraction precision indicating whether or not the characteristic portion of the person can be extracted, with respect to the feature information extracted by the feature extraction unit based on a position of the characteristic portion.