Challenge-Response Object Recognition Without Stored Biometrics
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Solution Overview
Problem
Conventional object recognition systems face challenges in accurately identifying biological objects under varying conditions, such as changes in appearance due to aging, weathering, scale, orientation, and lighting, particularly in uncontrolled environments, and they raise privacy concerns by storing sensitive biometric data.
Innovation Solution
A challenge-response-pair (CRP) mechanism using a biometric print, like a human face, generates cryptographic keys without storing personal information, by applying random challenges to the biometric data and comparing responses to authenticate the object, even under different measurement conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems store biometric data for recognition, then recognition accuracy is improved, but privacy and security are compromised due to data leakage and theft risks
Solution Approach 1:
The patent extracts only the necessary functional characteristics (challenge-response pairs) from the complete biometric template, storing only these extracted features rather than the full biometric data. This allows recognition to proceed while minimizing stored information and reducing privacy risks.
Solution Approach 2:
The patent introduces challenge-response pairs as an intermediary mechanism between the stored biometric data and the recognition process. The stored data is transformed into cryptographic challenge-response pairs that mediate the authentication process, preventing direct access to raw biometric information while maintaining recognition functionality.
2Reliability
If biometric data is stored in databases, then recognition functionality is enabled, but the system becomes vulnerable to data breaches and unauthorized access
Solution Approach 1:
The patent transforms the storage parameters from raw biometric images to processed challenge-response pairs. This parameter change in data representation maintains the functional capability for recognition while fundamentally altering the data structure to be more resistant to security breaches and unauthorized access.
Solution Approach 2:
The patent creates a functional copy of the biometric data in the form of challenge-response pairs that can be stored and processed without requiring the original biometric templates. This copy performs the necessary recognition function while being inherently more secure for storage purposes.
3Measurement precision
If conventional recognition systems use fixed templates, then initial recognition works, but accuracy degrades when objects change appearance due to aging, weathering, or damage
Solution Approach 1:
The patent implements a dynamic recognition approach where multiple challenge-response pairs are generated and compared, allowing the system to adapt to appearance variations. The system can dynamically select and weight different feature sets based on current conditions, maintaining accuracy despite aging, weathering, or damage to the object.
Solution Approach 2:
The patent segments the biometric data into multiple independent challenge-response feature sets rather than relying on a single fixed template. This segmentation allows different portions of the biometric information to be independently evaluated, improving robustness against appearance changes in any single region or feature.
Data Source
AI summary
An object recognition arrangement includes an enrollment process involving taking a biometric print of an object, which may be a biological object, generating a random seed, deriving challenges from the seed and using the challenges to measure the print resulting in a set of responses. A key is generated and used to encrypt a file. The key is then used to filter the response set, and a filtered subset is retained. Later, during a recognition process, another biometric print is taken of the object, the challenges are generated and applied to the new print, and a full set of responses are measured. The key is then recovered by comparing the full set of responses to the subset. The subset-superset response comparison may be done with multiple response streams, and the best stream is selected based on counting the number of a first binary symbol in the stream.