Biometric Key Generation via Quadrant Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating cryptographically strong cryptographic keys from biometric data require significant biometric data or precise feature extraction, making them difficult to implement effectively.
Innovation Solution
A method that segments biometric images into quadrants, detects features, generates subkey values based on quadrant locations and types, and concatenates these values to produce a cryptographic key, allowing for easy and effective key generation using biometric data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to generate cryptographically strong keys from biometric data, then key strength is improved, but data requirement and implementation complexity increase
Solution Approach 1:
The patent divides the biometric image into multiple quadrants (e.g., four quadrants) and extracts features independently from each quadrant. Each quadrant contributes a portion of the final cryptographic key through subkey generation. This segmentation allows the system to generate sufficient entropy for strong cryptography without requiring the entire high-resolution biometric image, thus reducing the effective data requirement while maintaining key strength.
Solution Approach 2:
The patent extracts specific features (such as ridge endings, bifurcations, or other distinctive biometric features) from the biometric image and uses only these extracted features for key generation, rather than processing the entire biometric dataset. This extraction approach concentrates the essential information needed for cryptography into a smaller, more manageable set of features, reducing the quantity of biometric data that must be processed while maintaining cryptographic strength.
2Reliability
If precise feature extraction is performed to ensure key strength, then cryptographic reliability is improved, but implementation difficulty increases
Solution Approach 1:
By dividing the biometric image into quadrants and processing each quadrant independently, the patent simplifies the feature extraction process. Each quadrant can be processed with simpler, less computationally intensive algorithms since the search space is reduced. The segmented approach maintains reliability because features from multiple quadrants are combined to form the final key, providing sufficient entropy without requiring complex extraction from the entire image.
Solution Approach 2:
The patent applies different processing or weighting to different quadrants based on their local characteristics. Each quadrant may have different feature densities or types, and the system can optimize extraction for each local region independently. This local quality approach allows simpler extraction methods to be applied in regions where they are sufficient, reducing overall implementation complexity while maintaining the cryptographic strength contributed by each region.
Data Source
AI summary
A system and method for generating an encryption key using physical characteristics of a biometric sample is described. In one embodiment, the biometric feature(s) from a sample are analyzed to generate a feature vector. After discretizing the feature(s), the resultant feature vector is translated into a bit vector. The bit vector is the secure biometric key that results from the biometric(s). The secure biometric key is used to generate at least one cryptographic key. A similar process is used to access the cryptographic key secured by the secure biometric key. If the access biometric key matches the secure biometric key, the cryptographic key is revealed and access is allowed. In another embodiment, if the access biometric key does not match the secure biometric key a camouflaging process is used to provide an unauthorized user a bogus secure biometric key indistinguishable from the correct secure biometric key.