Optical Security Feature Quality Verification via Pixel Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for checking the manufacturing quality of optical security features on documents of value, such as banknotes, are inefficient and prone to errors, particularly with optically variable data (OVD) features that require precise optical properties verification.
Innovation Solution
A method utilizing pixel data from spatially resolved images to assess the quality of optical security features by determining if a sufficient number of pixels within specified reference ranges exceed minimum hit and scatter values, indicating adequate manufacturing quality, and forming a quality signal for authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to check optical security features, then the checking process is simple, but the accuracy and reliability of quality verification deteriorates
Solution Approach 1:
The patent replaces manual visual inspection and simple optical checks with an automated digital image processing system. Pixel data from captured images is processed using computational methods to evaluate optical properties, substituting mechanical/optical inspection methods with electronic analysis to improve measurement precision while managing complexity through automation.
Solution Approach 2:
The patent transforms physical optical properties of security features into digital pixel data parameters. By capturing images and converting optical characteristics (color, brightness, patterns) into quantifiable pixel values, the system enables precise computational analysis of security feature quality without requiring complex physical measurement apparatus.
2Productivity
If manual inspection methods are used, then the equipment required is simple, but the productivity and efficiency of quality checking deteriorates
Solution Approach 1:
The system performs self-service quality assessment by automatically capturing images, processing pixel data, and determining compliance with security specifications without requiring manual intervention. The automated evaluation algorithm independently analyzes optical properties and generates quality assessments, significantly improving productivity while the complexity is managed through integrated software automation.
Solution Approach 2:
Manual quality checking operations are replaced with automated digital image processing and computational analysis. The system uses software-based evaluation of pixel data to assess security features, eliminating the need for manual inspection while improving throughput and consistency, with complexity managed through automation integration.
3Reliability
If simple inspection methods are used, then the operation is easy, but the ability to detect manufacturing defects deteriorates
Solution Approach 1:
Simple visual inspection is replaced with automated digital image processing that systematically analyzes pixel data for manufacturing defects. The computational method objectively evaluates optical properties and identifies anomalies that would be difficult to detect manually, improving reliability while the automated operation maintains simplicity through integration.
Solution Approach 2:
The system provides objective feedback through automated analysis of pixel data, comparing measured optical properties against specified requirements. This feedback mechanism reliably identifies manufacturing defects by systematically evaluating compliance, while the automated nature maintains operational simplicity without requiring expert judgment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method allows for quick and accurate verification of manufacturing quality, reducing the likelihood of counterfeit documents by ensuring that the security features meet specific optical property criteria, thereby enhancing authentication reliability.
Implementation Method 1
These optical properties can be, in particular, reflection, transmission, and/or luminescence properties
Implementation Method 2
These optical properties can be, in particular, reflection, transmission, and/or luminescence properties
Implementation Method 3
with an optical sensor for capturing an image with pixels, the pixel data of which are each assigned to locations in or on the section and represent optical properties of the security document at the locations
Data Source
Figure 1~4
Figure 5
Figure 6(a)~6(b)
AI summary
The invention relates to a method for examining the production quality, preferably the printing quality, of a predefined optical security feature in or on a predefined section of a valuable document, on the basis of pixel data of pixels of an image of said predefined section, wherein these data are associated, in each case, with locations in or on said section and reproduce optical characteristics of the valuable document at those locations. In this method, an examination takes place as to whether a first count, or a first portion, of those pixels of the image pixels with pixel data lying, according to a first predefined criterion, within a predefined first reference range for the security feature, exceeds a predefined first target minimum value for said security feature, and whether a first dispersion of the pixel data of those pixels lying, according to said first criterion, within the first reference range for the pixel data, exceeds a predefined first minimum dispersion for the security feature. Depending on the result of this examination, a quality signal is generated which only indicates that the printing quality is sufficient if the first count or the first portion exceeds the first target minimum value and the dispersion exceeds the first minimum dispersion.