Biometric Matching Cluster Configuration
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Solution Overview
Problem
Biometric matching systems face challenges in efficiently processing large volumes of data for de-duplication and search operations, requiring a balance between accuracy and computation time, which is typically achieved through multi-level hierarchical clustering but requires optimal configuration settings.
Innovation Solution
A method that receives biometric profiles data, sets accuracy and computing resource usage values, and performs biometric authentication by determining the number of clusters and levels for clustering, optimizing the configuration to ensure accurate matching with reduced resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multi-level hierarchical clustering is used to process large volumes of biometric data, then computation time is reduced, but accuracy may deteriorate without optimal configuration
Solution Approach 1:
The system dynamically adjusts clustering configuration parameters (number of clusters, levels, search depth) based on the specific requirements of each biometric matching task. This allows the system to optimize the balance between computation time and matching accuracy for different scenarios, rather than using fixed configuration settings.
Solution Approach 2:
The patent systematically varies key parameters including the number of clusters at each level, the depth of hierarchical levels, and the number of clusters searched during transactions. By changing these parameters, the system finds optimal configurations that reduce computation time while preserving matching accuracy for large biometric datasets.
2Measurement precision
If the number of clusters and levels in hierarchical clustering is increased to improve accuracy, then computation resources are consumed
Solution Approach 1:
The system performs partial clustering operations by limiting the number of clusters searched at each level and controlling the depth of hierarchical levels. This partial action approach provides sufficient matching accuracy for most biometric authentication scenarios while significantly reducing the computation resources required compared to exhaustive clustering of the entire dataset.
Solution Approach 2:
The biometric dataset is segmented into multiple clusters organized in a hierarchical structure. This segmentation allows the system to process data in manageable portions rather than treating the entire large dataset as a single unit, reducing memory requirements and computation resources while maintaining the ability to achieve accurate matches through the hierarchical organization.
3Measurement precision
If iterative processing over the whole dataset is performed to ensure accurate matching, then computation time increases significantly
Solution Approach 1:
The system performs preliminary clustering operations to organize biometric data into a hierarchical structure before actual matching transactions occur. This preliminary organization creates an efficient search framework that enables fast retrieval and comparison operations, avoiding the need for iterative processing over the entire dataset during authentication transactions.
Solution Approach 2:
The hierarchical cluster structure acts as an intermediary between the raw biometric dataset and the matching operation. Instead of directly comparing query data against all stored biometric records, the system uses the cluster hierarchy to guide and accelerate the search process, significantly improving processing speed while maintaining matching accuracy.
Data Source
AI summary
Computer system for performing biometric matching in a way that balances accuracy level required in the biometric matching against computing resources (for example, processor cycles) that will be needed to match authentication requesters with profiles of authorized users. In some embodiments, this is achieved by controlling the number of clusters and/or the number of clusters to be searched pursuant to an authentication request.