Fingerprint Feature Point Extraction for Correction Work Estimation
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Solution Overview
Problem
In fingerprint image processing, rolled prints and slap prints from ten-print cards often have unclear or missing fingerprinted regions, making it difficult to estimate the correction work required, especially in regions unique to rolled prints.
Innovation Solution
An information processing device acquires and extracts feature points from both rolled and slap prints, estimating the correction work needed by calculating the ratio of non-corresponding feature points in common regions to determine the work amount for non-common regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correction work is performed on rolled prints with unclear or missing fingerprinted regions, then matching accuracy is improved, but the work amount varies significantly and cannot be estimated
Solution Approach 1:
The system performs preliminary extraction and comparison of feature points between rolled prints and slap prints before the actual correction work. By pre-identifying the number of non-corresponding feature points in common regions, the system estimates the required correction work amount in advance, allowing users to select ten-print cards with acceptable correction effort before committing to the correction process.
2Reliability
If correction work is performed on all regions of rolled prints, then complete fingerprint data is achieved, but it is difficult to estimate work amount on non-common regions
Solution Approach 1:
The system divides the rolled print into two distinct regions: common regions (overlapping with slap print) and non-common regions (unique to rolled print). By segmenting the analysis this way, the system can accurately estimate correction work for common regions through feature point comparison, and handle non-common regions separately with predetermined correction processes, simplifying the overall work estimation.
3Loss of time
If feature points are extracted and compared between rolled prints and slap prints, then work amount estimation becomes possible, but processing complexity increases
Solution Approach 1:
The system extracts only the essential feature points from both rolled prints and slap prints, and specifically extracts only the non-corresponding feature points in common regions for comparison. This selective extraction approach provides sufficient information for work amount estimation without requiring complete analysis of all image data, thereby limiting processing complexity while achieving the estimation goal.
Data Source
AI summary
An information processing device includes: an acquisition unit that acquires a rolled print and a slap print on the same finger; an extraction unit that extracts first feature points from the rolled print and second feature points from the slap print, respectively; and an estimation unit that, based on a ratio occupied by the first feature points having no correspondence with the second feature points out of the first feature points included in a common region that is common to the rolled print and the slap print, estimates a first work amount of correction work performed by a user on a non-common region excluding the common region from the rolled print.