Substrate-Based Authenticity Collation Despite Surface Imperfections
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Solution Overview
Problem
Existing simple information media, such as visitor cards, are susceptible to forgery and suffer from decreased collation accuracy due to surface stains or scratches, particularly when used for short-term events.
Innovation Solution
An authenticity collation system and method that uses a substrate-based collation image printed on an information medium, employing a first processor to capture an initial image, a second processor to correct for density changes based on elapsed time, and determine a collation region by analyzing pixel differences and spatial frequency characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple information medium (substrate with printed collation image) is used instead of an IC memory embedded ID card, then the cost and issuance time are reduced, but the security and anti-forgery capability deteriorate
Solution Approach 1:
The system captures a reference image of the collation region immediately after printing the information medium, storing it as a baseline for future comparison. This preliminary action enables later authenticity verification without requiring complex embedded security features during issuance
Solution Approach 2:
The system detects and compensates for density changes in the printed collation image over time by comparing color and density characteristics between the reference image and subsequent captured images. This allows the system to distinguish between legitimate aging (uniform density changes) and forgery (non-uniform patterns)
2Device complexity
If the collation region on the information medium is used for authenticity verification, then the collation process is simplified, but collation accuracy deteriorates due to surface stains or scratches
Solution Approach 1:
The system extracts and compares only specific feature characteristics (color values, density distributions, spatial frequency patterns) from the collation region, rather than requiring perfect visual matching of the entire image. This extraction of key features enables accurate collation even when parts of the surface are stained or scratched
Solution Approach 2:
The system performs iterative comparison between the reference image and captured images, using feedback from density change detection to adjust collation parameters and identify the most reliable comparison metrics, thereby maintaining accuracy despite surface imperfections
3Measurement precision
If density correction based on elapsed time is performed, then collation accuracy is improved by compensating for printing density changes, but the processing complexity increases
Solution Approach 1:
The system pre-captures a reference image immediately after printing and stores it with timestamp information. This preliminary action establishes a baseline that automatically accounts for the initial printing state, eliminating the need for complex real-time density monitoring during collation
Solution Approach 2:
The system compensates for density changes by comparing temporal parameters (elapsed time since printing) and adjusting density thresholds accordingly. This parameter-based approach simplifies processing compared to complex image restoration techniques
Data Source
AI summary
An issuing apparatus is configured to acquire an image obtained by capturing a collation image printed on an information medium via a camera as a first captured image. A collation apparatus is configured to acquire an image obtained by capturing a collation image printed on an information medium to be collated via a camera as a second captured image, generate a corrected image obtained by performing density correction on the first captured image, by predicting a density change at a time point at which the second captured image is acquired based on an elapsed time from a time point at which the first captured image is acquired, and determine a collation region for collation between the first captured image and the second captured image based on a difference image obtained by obtaining a difference between the second captured image and the corrected image for each pixel.