Biometrics Authentication System Using Dynamic Threshold Segmentation
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Solution Overview
Problem
Biometrics authentication systems face challenges in accurately verifying individuals due to variations in body state, leading to potential false negatives or reduced precision when similarity thresholds are adjusted, which can result in user inconvenience and decreased verification speed.
Innovation Solution
A biometrics authentication system that detects and verifies multiple biometric data sets for each user, using pattern recognition to calculate similarity and identify individuals without relying on strict threshold matching, by registering and verifying multiple biometric data types and utilizing a link table to associate individual data with biometric data, ensuring accurate authentication without modifying similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the similarity threshold is raised to prevent false positives when verifying biometric data, then authentication accuracy for other persons is improved, but verification speed and user convenience deteriorate
Solution Approach 1:
The patent segments the authentication process into two distinct phases: registration phase where similarity thresholds are raised to prevent false positives, and verification phase where the original lower threshold is restored to maintain speed. This segmentation allows each phase to optimize for its specific goal without compromising the other.
Solution Approach 2:
The patent performs preliminary filtering during the registration phase by raising the similarity threshold, which pre-identifies and flags potential false positive cases. This preliminary action allows the verification phase to operate at full speed while already having a filtered set of candidates to evaluate, thus maintaining both accuracy and speed.
2Productivity
If the similarity threshold is lowered to improve verification speed and user convenience, then authentication processing time is reduced, but authentication accuracy and reliability deteriorate
Solution Approach 1:
The patent divides the authentication system into registration and verification modules with different threshold settings. The verification module uses a lower threshold for speed, while the registration module uses a higher threshold for accuracy, eliminating the need to choose one setting over the other permanently.
Solution Approach 2:
The patent makes the similarity threshold dynamic rather than static, allowing it to change based on the operational phase (registration vs. verification). This dynamic adjustment enables the system to optimize for accuracy during registration and for speed during verification, resolving the contradiction between these two requirements.
3Measurement precision
If strict threshold matching is used to ensure precise biometric verification, then measurement precision is improved, but the system becomes sensitive to body state variations causing false negatives
Solution Approach 1:
The patent changes the similarity threshold parameter based on the operational context: using a higher threshold during registration to ensure precision and filter false positives, and a lower threshold during verification to accommodate natural body state variations and prevent false negatives, thus maintaining both precision and reliability.
Data Source
AI summary
A biometrics authentication system detect body characteristics, verify the characteristics against registered biometrics data to perform individual authentication. In 1:n authentication, a plurality of different type biometrics database sets of a user are registered in a database, and at the time of verification of one type biometrics data set with one type registered biometrics data set, the control unit judges whether similar biometrics data sets of other person are registered, and if other person's similar biometrics data sets are registered, performs verification of each of the another type biometrics data sets of the plurality of similar biometrics data sets with the detected another type biometrics data set, and identifies the individual in question.