Biometric Threshold Calibration via Gaussian Score Analysis
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Solution Overview
Problem
Biometric authentication systems, such as voice authentication, face challenges in maintaining accuracy due to changes in telephony systems or codecs, leading to increased false negatives and false positives, which require manual recalibration that is time-consuming and inefficient.
Innovation Solution
An automated system that adjusts biometric thresholds by collecting and analyzing authentication and enrollment voice prints, generating imposter scores through random pairings, and using a Gaussian mixture model to update the distribution parameters, allowing for periodic and accurate recalibration without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recalibration is performed to adjust biometric thresholds, then system accuracy is improved, but time consumption and resource usage increase
Solution Approach 1:
The system automatically performs recalibration by collecting biometric scores, generating synthetic imposter scores, fitting Gaussian distributions, and adjusting thresholds without human intervention. The biometric system serves itself by autonomously detecting accuracy degradation and executing the full recalibration workflow including data collection, analysis, and parameter adjustment.
Solution Approach 2:
The system continuously monitors biometric comparison scores and uses statistical analysis to detect when accuracy degradation occurs. This feedback loop triggers automatic recalibration when performance metrics fall below thresholds, creating a closed-loop system that self-corrects based on observed performance.
2Measurement precision
If manual recalibration is performed to adjust biometric thresholds, then system accuracy is improved, but resource consumption increases
Solution Approach 1:
The system autonomously executes the complete recalibration process including collecting authentication biometric samples, generating synthetic imposter data through random pairings, fitting Gaussian distributions to score sets, and adjusting thresholds without requiring human resources. This eliminates the productivity loss associated with manual recalibration operations.
Solution Approach 2:
The system continuously collects and stores authentication biometric samples and scores in the background during normal operation, preparing the data sets needed for recalibration in advance. This preliminary data collection ensures that when recalibration is triggered, the analysis can proceed immediately without data gathering delays.
3Reliability
If threshold changes are made to adapt to telephony system changes, then false negatives and false positives are reduced, but system complexity increases
Solution Approach 1:
The system automatically detects when telephony system changes or codec modifications cause accuracy degradation by monitoring biometric score distributions. When degradation is detected, the system triggers recalibration to adjust thresholds, creating a feedback mechanism that maintains reliability without requiring manual system configuration or user intervention.
Solution Approach 2:
The system autonomously adapts to telephony system changes by continuously analyzing biometric score distributions and automatically adjusting thresholds when degradation is detected. This self-service adaptation eliminates the need for manual system configuration and reduces the operational complexity of maintaining reliability across different telephony environments.
4Measurement precision
If frequent recalibration is performed to maintain accuracy, then false reject and false accept rates are improved, but processing time increases
Solution Approach 1:
The system performs recalibration periodically based on monitored performance metrics rather than continuously or on a fixed schedule. Recalibration is triggered when statistical analysis detects accuracy degradation beyond predefined thresholds, optimizing the balance between maintaining low false reject and false accept rates and preserving processing throughput by avoiding unnecessary recalibration operations.
Data Source
AI summary
A system and method may adjust the threshold or other settings in a biometric comparison system, or provide a report or display of parameters. Over a series of comparisons of authentication biometric samples (e.g. authentication VPs) to enrollment biometric samples (e.g. enrollment VPs), the authentication samples may be stored, and scores resulting from the biometric comparisons may be stored in a first set of scores. A second set of biometric comparisons may be created, each using a pairing of a stored authentication biometric sample and an enrollment biometric sample, each biometric comparison resulting in a score, the scores forming a second set of scores. The first and second sets of scores may be combined to produce a third set of scores, and an iterative process may be performed over the third set of scores to update the parameters of the Gaussian distribution of the third set of scores.