Biometric Query Plan Generator for Multi-Modal Search Optimization
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Solution Overview
Problem
Biometric systems face challenges in accurately and efficiently matching biometric samples due to poor quality samples, lack of searchable indices, and inability to perform multi-modal searches, leading to slow and resource-intensive brute-force searches.
Innovation Solution
A method and system for generating a biometric query plan that receives biometric sample quality information and search performance parameters to create a customized query plan, prioritizing queries for speed or accuracy, and enabling multi-modal searches by characterizing samples and techniques to optimize matching processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute-force biometric searches are performed, then all biometric samples in the gallery are searched, but the search speed is slow and processing resources are excessive
Solution Approach 1:
The patent segments the biometric search process into multiple stages: first filtering samples based on quality metrics and modality compatibility, then performing detailed matching only on the filtered subset. This segmentation reduces the search space from all gallery samples to only those with potential matches, improving search speed while maintaining reliability.
Solution Approach 2:
The system performs preliminary actions by pre-characterizing biometric samples and organizing them by modality and quality metrics before the actual search. This preliminary organization enables rapid filtering and eliminates the need to evaluate every sample in detail during the search phase, thus improving productivity without sacrificing completeness.
2Ease of operation
If poor quality biometric samples are used, then the search can be performed, but the identification accuracy decreases and matching errors increase
Solution Approach 1:
The patent applies local quality by evaluating and weighting different aspects of biometric sample quality (such as resolution, completeness, and clarity) separately. High-quality regions or features of a sample are given more weight in the matching process, while poor-quality regions are downweighted or excluded, thereby maintaining identification accuracy even when overall sample quality is moderate.
Solution Approach 2:
The system dynamically adjusts matching parameters and thresholds based on the quality metrics of the input biometric sample. When sample quality is low, the system modifies search parameters to be more selective or adjusts decision thresholds to compensate for quality deficiencies, thereby maintaining identification accuracy across varying sample qualities.
3Adaptability or versatility
If biometric samples are not organized with indices, then all samples can be searched, but the search process becomes resource-intensive and inefficient
Solution Approach 1:
The patent creates a multi-functional indexing system that organizes biometric samples by multiple attributes simultaneously (modality, quality metrics, demographic characteristics). This universal index structure enables the system to perform different types of searches efficiently without requiring separate indexing schemes, thus reducing processing resources while maintaining search flexibility.
Solution Approach 2:
The system performs preliminary organization of biometric samples into indexed structures based on modality and quality characteristics before searches are executed. This pre-organization enables rapid retrieval and filtering during the search phase, significantly reducing the computational resources required while preserving the ability to perform various types of searches.
4Device complexity
If single-modal biometric searches are performed, then the search process is simpler, but the system cannot perform multi-modal searches and identification accuracy is limited
Solution Approach 1:
The patent segments the biometric data into multiple modalities (e.g., facial recognition, fingerprint, iris) and processes each modality separately through dedicated matching algorithms. The results from different modalities are then combined and weighted to produce a final identification decision. This segmentation allows the system to handle complex multi-modal searches while maintaining manageable complexity through modular processing.
Solution Approach 2:
The system merges results from multiple biometric modalities by combining match scores and applying fusion rules. This merging process leverages the strengths of different modalities to improve overall identification reliability and accuracy, allowing the system to overcome the limitations of any single modality while keeping the search process organized and manageable.
Data Source
AI summary
According to one embodiment, a computer-readable medium includes computer-executable instructions that, when executed by a computer, are configured to receive biometric sample quality information. The biometric sample quality information describes quality of one or more biometric samples. Search performance parameters are received. A biometric query plan is generated in compliance with the search performance parameters. The biometric query plan provides a search plan for identifying potential matches to the one or more biometric samples.