Pseudo-Vascular Pattern Generation With Dual Random Noise
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Solution Overview
Problem
The generation of pseudo-vascular patterns for biometric authentication systems faces challenges due to the high time and cost of collecting actual vascular patterns from biological bodies, and the difficulty in storing such data due to privacy and regulatory constraints, while existing mathematical models lack diversity and feasibility in evaluations.
Innovation Solution
A pseudo-vascular pattern generator uses a processor to generate pseudo-vascular patterns by combining random noise images based on different random seeds, applying filters to emphasize blood vessels, and setting regions of interest, allowing for diverse pattern generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual vascular patterns are collected from captured images of biological bodies, then the data can be used for biometric authentication algorithm development, but enormous time and costs are required and storage is restricted due to privacy and regulatory constraints
Solution Approach 1:
The patent generates pseudo-vascular patterns that replicate the statistical properties and structural characteristics of actual vascular patterns without using real biological data. This copying approach allows algorithm development while avoiding the time-consuming collection and storage issues of actual vascular pattern data
Solution Approach 2:
Instead of relying on expensive and difficult-to-obtain actual vascular pattern data, the patent uses computationally-generated pseudo-patterns that can be produced rapidly and disposed of after use, eliminating storage restrictions and reducing collection time
2Ease of manufacture
If pseudo-vascular patterns are generated using mathematical models such as reaction-diffusion equations, then data storage restrictions are avoided, but the generated patterns lack diversity and do not closely resemble actual vascular patterns
Solution Approach 1:
The patent employs multiple different filters with varying parameters (Gaussian blur, bilateral filter, median filter, etc.) and adjusts their parameters dynamically during the generation process. This parameter variation enables diverse pseudo-vascular pattern generation while maintaining computational efficiency
Solution Approach 2:
The generation process incorporates dynamic elements including random seed initialization, iterative filtering operations, and adaptive parameter adjustment. These dynamic mechanisms ensure each generated pattern is unique and diverse, overcoming the static nature of traditional mathematical models
3Manufacturing precision
If multiple filters are applied sequentially to generate pseudo-vascular patterns, then pattern diversity and realism are improved, but the generation process complexity increases
Solution Approach 1:
The complex generation process is segmented into distinct sequential steps: initial pattern generation, multiple filtering stages, thresholding, and refinement. Each segment performs a specific function, making the overall complex process manageable and controllable while achieving high realism
Data Source
AI summary
In a pseudo-vascular pattern generator, a processor is configured: to generate a first random number based on a first random seed and to generate a second random number based on a second random seed that is different from the first random seed; to generate a first image by adding first noise that is based on the first random number to a gray image; to generate a second image by adding second noise that is based on the second random number to the gray image; to generate a third image by combining the first image and the second image; and to generate a pseudo-vascular pattern image that is an image including a pseudo-vascular pattern by using the third image.