Neuroplastic Biometric Security via Dynamic Extractor Mapping
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Solution Overview
Problem
Biometric security systems are vulnerable to spoofing, template interception, and false accept/false negative rates due to weaknesses in feature vector extraction and matching processes, particularly at the sensor and database levels.
Innovation Solution
The implementation of neuroplastic fidelity using unsupervised and supervised machine learning to dynamically select and map feature extractors and matchers, generating secure data templates through a probabilistic neural network and genetic algorithm, which enhances the resilience of biometric systems by mimicking the brain's adaptability and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional biometric matching systems use fixed feature extractors and matchers, then the system is simple to implement, but the system becomes vulnerable to spoofing and template manipulation attacks
Solution Approach 1:
The patent implements dynamic selection of feature extractors and matchers based on input characteristics. Instead of using fixed algorithms, the system dynamically chooses appropriate extractor/matcher pairs from multiple options, adapting to different biometric modalities and attack scenarios. This dynamic approach prevents attackers from exploiting static system weaknesses while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system changes operational parameters by selecting different feature extractor and matcher configurations based on the specific biometric input and security requirements. Rather than maintaining a single fixed configuration, the system adjusts its processing parameters dynamically, choosing optimal algorithms for each situation. This parameter variation makes the system resilient to attacks that target specific algorithmic weaknesses.
2Reliability
If biometric templates are stored in centralized databases, then data access is efficient, but the system becomes vulnerable to template interception and database attacks
Solution Approach 1:
The patent segments the biometric data processing into distinct functional components: feature extraction, template generation, and matching. Each component operates independently with defined interfaces, allowing the system to process biometric data through multiple extractor/matcher pairs without requiring a single centralized template storage point. This segmentation reduces the attack surface for template interception while maintaining efficient data access through modular architecture.
Solution Approach 2:
The system introduces intermediary processing layers between raw biometric data and template storage/matching. Multiple feature extractors process input data through different transformation paths, and multiple matchers evaluate templates through various comparison methods. These intermediary processing stages obfuscate the direct path to templates, making interception and manipulation more difficult while preserving matching efficiency through the structured intermediary architecture.
3Adaptability or versatility
If a single extractor/matcher pair is used for biometric matching, then the processing is fast and simple, but the system lacks adaptability to different attack vectors
Solution Approach 1:
The patent implements a universal framework where multiple feature extractors and matchers are integrated into a single cohesive system. Each extractor and matcher is designed to handle specific biometric modalities and attack scenarios, yet all work together through standardized interfaces. This multi-functionality allows the system to adapt to different attack vectors by selecting appropriate component pairs while maintaining efficient processing through the unified framework.
Solution Approach 2:
The system employs partial action by selecting and activating only the necessary extractor/matcher pairs for each specific biometric input and security context, rather than running all possible combinations. This selective activation maintains processing speed by avoiding unnecessary computations while still providing comprehensive security coverage through the availability of multiple specialized components ready for deployment when needed.
Data Source
AI summary
A system, method and program product for providing biometric security using neuroplastic fidelity. A method is disclosed that includes: receiving biometric data; analyzing the biometric data with a probabilistic neural network and outputting a chromosome containing a binary string; mapping the binary string to a selected extractor and a selected matcher; apply the selected extractor to the biometric data to generate a template; using the selected matcher to compare the template to a set of stored templates to identify a match; and outputting a result.