Fingerprint Central Line Image Generation via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fingerprint collation systems require manual editing of central line images, which is burdensome for operators and reduces accuracy, as they need to extract feature points like branch and end points from fingerprint images with high precision.
Innovation Solution
An image processing device utilizing machine learning to generate a binary central line image from a fingerprint image, incorporating central line and zone information to enhance feature point extraction accuracy, thereby reducing operator burden and improving image processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual editing is performed to increase feature point extraction accuracy, then extraction accuracy is improved, but operator burden increases
Solution Approach 1:
The system performs automatic central line image generation and feature point extraction without requiring operator intervention. The extraction unit automatically extracts feature points from the generated central line image, eliminating the need for manual editing while maintaining high extraction accuracy.
Solution Approach 2:
The patent replaces the mechanical manual editing process with an automated image processing system. The central line image generation unit and extraction unit work together to automatically generate central line images and extract feature points, substituting human operators with computational algorithms.
2Measurement precision
If manual editing is performed to increase feature point extraction accuracy, then extraction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs continuous automatic processing from central line image generation to feature point extraction without interruption. The extraction unit continuously extracts feature points from the generated central line images, maintaining high accuracy while reducing total processing time by eliminating manual editing steps.
Solution Approach 2:
The central line image generation unit performs preliminary processing to generate high-quality central line images before the extraction unit operates. This preliminary action ensures that the extraction unit receives optimized input data, improving extraction accuracy while reducing the time needed for subsequent processing.
3Ease of operation
If automatic central line image generation is performed, then operator burden is reduced, but image quality may deteriorate
Solution Approach 1:
The system uses feedback mechanisms where the extraction unit evaluates the generated central line images and adjusts processing parameters to maintain high image quality. The feedback loop ensures that automatic generation produces images suitable for accurate feature point extraction without requiring manual intervention.
Solution Approach 2:
The central line image generation unit dynamically adjusts processing parameters to optimize image quality for different fingerprint patterns. By changing parameters such as threshold values and processing intensity, the system maintains high image quality across various input conditions while operating automatically.
Data Source
AI summary
An image processing device outputs, by using a result of machine learning, an image including three or more gradations and indicating a central line in response to an input of a fingerprint image, the machine learning being performed by using data including at least a fingerprint image, central line information indicating a central line in the fingerprint image, and zone information indicating a zone that is a portion of the fingerprint image which is effective for fingerprint collation, and generates a binary central line image based on the output image.