Dictionary Learning for Biometric Pattern Matching Accuracy
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Solution Overview
Problem
Pattern matching processing in biometrics authentication is prone to errors due to quality degradation of patterns, such as blurry images, leading to incorrect determinations of identical patterns.
Innovation Solution
A dictionary learning device and apparatus that calculate a matching score and learn a quality dictionary to evaluate the degradation degree of patterns, enabling compensation of scores for improved accuracy in pattern matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching is performed using traditional methods without quality evaluation, then the processing is simple and fast, but the accuracy deteriorates when patterns are degraded (blurry, indistinct)
Solution Approach 1:
The patent applies preliminary action by performing quality evaluation of patterns before conducting pattern matching. The system calculates quality metrics (such as sharpness, contrast, or other degradation indicators) of the input patterns in advance, and uses these quality evaluations to adjust the matching process or threshold values. This preliminary quality assessment allows the system to compensate for degraded patterns before they affect matching accuracy, thereby improving reliability without requiring complex real-time adjustments during matching.
2Reliability
If quality evaluation and score compensation are added to pattern matching, then the reliability improves, but the processing time and computational load increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting matching threshold values or score compensation parameters based on the evaluated quality of input patterns. When patterns are detected to be of low quality (blurry, indistinct), the system modifies the decision thresholds or applies compensation factors to the matching scores. This allows the system to maintain high reliability across varying pattern qualities without requiring completely different processing algorithms, thereby balancing reliability improvement with computational efficiency.
Data Source
AI summary
Provided is a technology which enables further improvement of the accuracy of the determination in the pattern matching processing. A dictionary learning device 1 includes a score calculation unit 2 and a learning unit 3. The score calculation unit 2 calculates a matching score representing a similarity-degree between a sample pattern, which is a sample of a pattern which is likely to be subjected to a pattern matching processing, and a degradation pattern resulting from a degrading processing on the sample pattern. The learning unit 3 learns a quality dictionary based on the calculated matching score and the degradation pattern. The quality dictionary is a dictionary which is used in a processing to evaluate a degradation degree (quality) of a matching target pattern of being pattern of an object on which the pattern matching processing is carried out.