Fingerprint Medium Classification Using Feature Matrix Detection
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Solution Overview
Problem
Existing fingerprint detection technologies cannot effectively identify the type of medium on which fingerprints are located, limiting further analysis and identification.
Innovation Solution
A fingerprint detection method involving obtaining native domain information, executing fingerprint feature extraction, and using a trained medium classification network to determine the medium type through a fingerprint medium classification vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fingerprint detection methods are used, then the detection process is simple, but the detection accuracy is low due to insufficient feature point extraction and matching
Solution Approach 1:
The patent segments the fingerprint detection process into multiple independent modules: image acquisition module, pre-processing module (including denoising and binarization), feature point extraction module, and matching module. Each module processes specific aspects of fingerprint analysis separately, improving overall detection accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces intermediate processing steps between image acquisition and feature point extraction, specifically denoising and binarization processes. These intermediary steps enhance the quality of input data for feature extraction, thereby improving detection accuracy without directly complicating the core matching algorithm.
2Measurement precision
If multiple processing steps are added to improve detection accuracy, then the detection precision increases, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the fingerprint image through denoising and binarization before feature point extraction. This preliminary processing simplifies the image data structure in advance, making subsequent feature extraction and matching operations more efficient and accurate, thereby reducing overall processing time despite adding initial steps.
Solution Approach 2:
The patent changes the parameter representation of fingerprint data through binarization, converting grayscale images to binary images with simplified pixel values. This parameter transformation reduces data complexity and enables faster processing in subsequent matching stages while maintaining or improving detection precision.
3Reliability
If traditional feature extraction methods are used, then the system is easy to implement, but the false acceptance rate and false rejection rate are high
Solution Approach 1:
The patent replaces traditional mechanical or simple algorithmic feature extraction methods with advanced image processing techniques including wavelet transform-based denoising and adaptive binarization algorithms. This substitution significantly improves detection reliability by extracting more robust feature points, while the modular implementation keeps system complexity manageable.
Data Source
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AI summary
Embodiments of the present application provide a fingerprint detection method and apparatus, a program product, and a storage medium. The method includes: obtaining native domain information of a to-be-detected fingerprint, where the native domain information is original data collected when the to-be-detected fingerprint is verified; executing fingerprint feature extraction on the native domain information to obtain a fingerprint feature matrix of the to-be-detected fingerprint; executing a classification operation on the fingerprint feature matrix through a trained medium classification network to obtain a fingerprint medium classification vector of the to-be-detected fingerprint, where the fingerprint medium classification vector is used for representing, in a vector form, a probability that the to-be-detected fingerprint belongs to a plurality of medium types respectively; and determining a medium type of the to-be-detected fingerprint based on the fingerprint medium classification vector. The present application solves the problem that the type of a fingerprint medium cannot be effectively detected in related technologies.