Biometric Threshold Calculation via Population Estimation
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Solution Overview
Problem
Existing biometric authentication systems fail to account for unknown others, leading to inefficiencies in threshold determination.
Innovation Solution
A threshold calculation system that includes a first acquisition unit for matching information, a second acquisition unit for attribute information, a storage unit, a sampling unit for extracting sample data based on attribute conditions, a population estimation unit, and a threshold calculation unit to determine thresholds based on the estimated population distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional threshold calculation methods are used based on registered data only, then the calculation process is simple, but the threshold accuracy is insufficient because unknown others are not considered
Solution Approach 1:
The system performs preliminary actions by collecting attribute information from multiple data sources before threshold calculation, and pre-processes this information to create population distribution models. This preliminary preparation enables the subsequent threshold calculation to account for unknown others without excessive complexity during the actual authentication process
Solution Approach 2:
The patent introduces attribute information and population distribution models as intermediary elements between the registered data and the threshold calculation. These intermediaries bridge the gap by representing characteristics of unknown others, allowing the system to calculate accurate thresholds without directly incorporating all possible unknown data
2Measurement precision
If population distribution estimation is performed to consider unknown others, then the threshold accuracy improves, but the calculation time increases
Solution Approach 1:
The system performs population distribution estimation as a preliminary action before actual threshold calculation. By pre-analyzing attribute information from multiple sources and establishing population models in advance, the system reduces the computational burden during real-time threshold calculation, thereby minimizing time loss while maintaining high accuracy
Solution Approach 2:
The patent applies partial action by selectively using attribute information from multiple data sources based on relevance and availability. Instead of processing all possible data equally, the system focuses on the most significant attributes for population distribution estimation, reducing calculation time while maintaining sufficient accuracy
3Measurement precision
If attribute information from multiple data sources is collected and analyzed, then the population distribution estimation accuracy improves, but the data processing complexity increases
Solution Approach 1:
The system segments the data processing task by dividing attribute information collection and analysis into distinct modules: data collection from multiple sources, attribute extraction, data cleaning, and population distribution modeling. This segmentation allows each module to handle specific aspects independently, reducing overall processing complexity while maintaining high estimation accuracy
Solution Approach 2:
The patent creates a universal data processing framework that can handle attribute information from multiple different data sources using the same methodology. This multi-functional approach consolidates diverse data processing tasks into a unified system, reducing complexity by avoiding separate processing pipelines for each data source
Data Source
AI summary
A threshold calculation system, includes: a first acquisition unit that obtains a matching information that is used for matching of a biological body; a second acquisition unit that obtains an attribute information indicating an attribute of the biological body; a storage unit that stores the matching information and the attribute information for each biological body; a sampling unit that extracts, as sample data, a plurality of matching informations from the storage unit, on the basis of a predetermined condition about the attribute information; a population estimation unit that estimates a population from the sample data; and a threshold calculation unit that calculates a threshold related to the matching information, on the basis of a distribution of the estimated population. According to such a threshold calculation system, it is possible to properly calculate the threshold related to biometric authentication.