Biometric Matching Index Table for Fast Pattern Search
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Solution Overview
Problem
Current fingerprint identification systems face challenges in speeding up the matching process without compromising accuracy, particularly as database sizes increase, and existing approaches like fingerprint classification and minutia information processing are either unreliable or slow.
Innovation Solution
A scalable and configurable system that uses an index table with quality quantities for each minutia of biometric images, generating patterns for comparison, and accumulating quality quantities to select candidate minutiae, thereby reducing the search space efficiently while maintaining high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If fingerprint classification (four-class or five-class scheme) is used to reduce the matched candidate list, then the matching time is reduced, but the classification reliability deteriorates causing misclassification
Solution Approach 1:
The fingerprint matching process is segmented into multiple stages: first using spatial patterns to create initial candidate lists, then using minutia information for refined matching. This segmentation allows the system to use less reliable but faster spatial patterns for initial filtering, then apply more accurate but slower minutia analysis only to promising candidates, resolving the contradiction between speed and reliability.
Solution Approach 2:
The system performs preliminary classification using spatial patterns before the main minutia-based matching process. This preliminary action filters out obviously non-matching fingerprints early, reducing the workload for subsequent detailed analysis while maintaining overall reliability through the two-stage approach.
2Measurement precision
If minutia information is used to reduce the number of matches, then the matching accuracy is improved, but the processing speed substantially decreases
Solution Approach 1:
The matching process is divided into two segments: a fast spatial pattern-based initial filtering stage that creates candidate lists, and a more accurate but slower minutia-based refinement stage that processes only the reduced candidate list. This segmentation allows the system to achieve high accuracy through minutia analysis while maintaining acceptable processing speed by limiting minutia analysis to fewer candidates.
Solution Approach 2:
The system applies partial minutia analysis only to the subset of fingerprints that pass the spatial pattern filter, rather than analyzing all fingerprints with full minutia detail. This partial action reduces the total computational load while maintaining the accuracy benefits of minutia-based matching for the relevant subset.
3Quantity of substance
If the database size increases to accommodate more fingerprints, then the system capacity is improved, but the matching time and processing complexity increase
Solution Approach 1:
The matching process is segmented into a fast initial filtering stage using spatial patterns that can handle large databases efficiently, and a more detailed minutia-based matching stage that processes only the reduced candidate list. This segmentation enables the system to maintain fast processing speeds even as the database size increases to accommodate more fingerprints.
Solution Approach 2:
The system performs preliminary spatial pattern-based filtering before minutia analysis, creating a reduced candidate list that is then processed with more detailed minutia matching. This preliminary action is crucial for maintaining performance as database size increases, as it filters out the majority of non-matching fingerprints early in the process.
Data Source
AI summary
A method and system for matching two biometric images including receiving an input biometric image; generating an index table for the input biometric image, wherein the index table includes a quality quantity for each minutia of the input biometric image; receiving a second biometric image; generating a number of patterns for a first minutia of the second biometric image; associatively accessing the index table by the generated number of patterns; accumulating quality quantities accessed from the index table for each minutia of the input biometric image for the number of patterns of the first minutia of the second biometric image; and selecting a minutia candidate of the input biometric image responsive to the accumulated quality quantities.