Fingerprint Imaging Ridge Flow Map Residual Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fingerprint imaging technologies face challenges in providing quick and accurate user identification and discrimination, especially under environmental conditions such as moisture, oil, and dirt, and often require partial sampling, which affects processing time and accuracy.
Innovation Solution
A fingerprint imaging system that includes a sensor to generate a ridge flow map and a processor to perform multi-layer decomposition of the image, characterizing image quality through first and second-order residuals, allowing for efficient user identification and authentication by measuring variance in these residuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-layer decomposition with higher-order residuals is used to improve discrimination capability, then user identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the fingerprint image analysis into multiple decomposition layers (first-order, second-order, and higher-order residuals). Each layer processes specific frequency components and structural features independently, allowing parallel computation and optimized processing pipelines that reduce overall processing time while maintaining high identification accuracy.
Solution Approach 2:
The patent performs preliminary quality assessment using lower-order residuals before proceeding to higher-order decomposition. This preliminary action filters out low-quality images early in the process, avoiding unnecessary computation of higher-order residuals for images that would be rejected anyway, thus reducing average processing time.
2Measurement precision
If higher-order residual decomposition is applied to characterize image quality, then discrimination capability is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex higher-order decomposition process into manageable segments corresponding to different residual orders. Each segment handles specific computational tasks with well-defined inputs and outputs, making the overall complex algorithm easier to implement, debug, and optimize on various hardware platforms.
Solution Approach 2:
The patent implements a flexible decomposition depth parameter that allows the system to perform only as many decomposition layers as needed for a given application. For simple authentication scenarios, only first or second-order residuals are computed, reducing complexity. For forensic or high-security applications, higher-order decomposition can be enabled to maximize discrimination capability.
3Measurement precision
If complete fingerprint sampling is performed to improve identification accuracy, then user discrimination is improved, but processing time increases due to larger data volume
Solution Approach 1:
The patent extracts only the essential features needed for identification from the complete fingerprint image through multi-layer residual decomposition. The first-order residuals capture dominant ridge patterns, second-order residuals capture local variations, and higher-order residuals capture fine details. This selective extraction maintains identification accuracy while reducing the effective data volume that requires intensive processing.
Solution Approach 2:
The patent computes decomposition layers selectively based on image quality and application requirements. For high-quality images, full decomposition to higher orders is performed. For lower-quality or routine authentication images, processing is truncated at lower orders, achieving partial action that balances accuracy with processing efficiency.
Data Source
AI summary
A fingerprint processing system comprises a fingerprint sensor configured to generate an image of a fingerprint, and a processor configured to process the fingerprint image. The processor is operable to generate a ridge flow map comprising ridge flow vectors characterizing the fingerprint, and a multi-layer decomposition based on the ridge flow vectors. The decomposition includes at least first and second-order residuals, based on the ridge flow vectors, and the processor is operable to characterize a quality of the fingerprint image, based on the residuals.