Interoperable Biometric Representation via Feature Mapping
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Solution Overview
Problem
Current biometric systems face challenges in achieving interoperability due to the use of disparate representation formats, which restricts cross-platform recognition and validation processes.
Innovation Solution
The system generates interoperable biometric representations by converting disparate formats into a common, privacy-secured format using feature-to-feature mapping functions and machine learning models, such as deep neural networks, to enable cross-vendor matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If biometric representations are transformed to proprietary formats to increase privacy and security, then privacy and security are improved, but interoperability between different biometric systems deteriorates
Solution Approach 1:
The patent introduces a common interoperable format that acts as an intermediary between proprietary biometric formats. This intermediate representation layer enables different vendors' proprietary formats to be transformed into a standardized format that maintains both security/privacy protections and cross-system compatibility, allowing biometric data to be exchanged and compared across different platforms without exposing raw biometric information
Solution Approach 2:
The patent transforms biometric representations by changing their format parameters from proprietary vendor-specific structures to a standardized common format. This parameter transformation involves converting biometric templates while preserving their essential characteristics and security properties, enabling interoperability without compromising the protective measures embedded in proprietary formats
2Adaptability or versatility
If machine learning models are trained to transform biometric representations between formats, then interoperability is improved, but computational complexity and training requirements worsen
Solution Approach 1:
The patent performs the complex transformation work in advance by pre-training machine learning models on large datasets of biometric representations from multiple vendors. These pre-trained models are then deployed as ready-to-use transformation engines that can convert between formats without requiring real-time complex computations, shifting the computational burden from operational use to offline training phase
Solution Approach 2:
The patent creates learned transformation functions that copy the essential mapping relationships between different biometric formats. Instead of implementing complex real-time transformations, the system uses pre-learned copy functions that replicate the transformation behavior, reducing computational overhead during actual interoperability operations
Data Source
AI summary
A process for interoperable biometric representation can include receiving a biometric representation in a first format. The process can include determining a dimension parameter based on the biometric representation, wherein the dimension parameter does not exceed a dimension of the biometric representation. The process can include generating a common biometric representation in a second format by applying a feature-to-feature mapping function to the biometric representation, wherein a vector dimension of the common biometric representation equals the dimension parameter. The process can include applying a lossy transformation to the common biometric representation to generate a token.