Fingerprint Image Block Segmentation for Fake Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Fingerprint recognition technologies struggle to differentiate between genuine and fake fingerprints, particularly when fake fingerprints are made from materials like rubber, silicon, gelatin, or latex, leading to misrecognition.
Innovation Solution
A method and apparatus that assess the image quality of input fingerprint images by dividing them into blocks, determining Image Quality Assessment (IQA) values, and using confidence determination models to differentiate between genuine and fake fingerprints based on these values, ensuring accurate verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fingerprint recognition methods are used, then the system is simple and easy to operate, but fake fingerprints made from materials like rubber, silicon, gelatin, or latex cannot be differentiated from genuine fingerprints
Solution Approach 1:
The fingerprint image is divided into multiple blocks, and each block is processed independently to extract local features. This segmentation allows the system to analyze different regions of the fingerprint separately, improving the detection of fake fingerprints while maintaining manageable computational complexity through localized processing.
Solution Approach 2:
The patent extends traditional 2D fingerprint analysis by incorporating 3D depth information and multiple imaging dimensions. By analyzing fingerprint images from different angles and depths, the system can detect subtle differences between genuine and fake fingerprints that are not visible in standard 2D views, thereby improving reliability without excessive complexity increase.
2Measurement precision
If image quality assessment is performed on the entire fingerprint image, then the detection accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The fingerprint image is divided into multiple blocks, and image quality assessment is performed on each block independently rather than on the entire image. This allows parallel processing of different regions, reducing overall verification time while maintaining high detection precision through comprehensive local analysis.
Solution Approach 2:
The system performs image quality assessment selectively on critical blocks or regions of the fingerprint image rather than uniformly processing all areas. By focusing computational resources on blocks that contain discriminative features or show signs of being fake, the system achieves high detection precision with reduced processing time.
Data Source
AI summary
An apparatus and method for detecting a fake fingerprint is disclosed. The apparatus may divide an input fingerprint image into blocks, determine an image quality assessment (IQA) value associated with each block, determine a confidence value based on the IQA values using a confidence determination model, and determine whether an input fingerprint in the input fingerprint image is a fake fingerprint based on the determined confidence value.