Fingerprint Recognition Using Mesoscopic Feature Extraction
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Solution Overview
Problem
Conventional fingerprint recognition technologies face challenges with accuracy and speed as fingerprint databases grow, requiring extensive expert labor and failing to leverage past data and experiences, especially in large-scale latent fingerprint matching.
Innovation Solution
Integration of mesoscopic features with advanced big data processing and deep learning techniques, including space-frequency framelet representations and neural networks, to enhance fingerprint matching speed and accuracy, automating the process and improving with continued use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional minutia-based feature marking is used for fingerprint matching, then the process can be completed with existing technology, but the accuracy deteriorates as the number of fingerprints in the database increases and the process requires extensive expert labor
Solution Approach 1:
The system uses automated deep learning models to extract mesoscopic features from fingerprint images, eliminating the need for expert manual marking. The neural network automatically identifies and extracts relevant features, making the system self-sufficient and removing dependency on expert labor while maintaining or improving accuracy even as database size increases
Solution Approach 2:
The patent replaces the manual mechanical process of expert feature marking with an automated computational system based on deep learning. The neural network automatically processes fingerprint images and extracts mesoscopic features, substituting human expert analysis with an automated algorithmic approach that scales efficiently with database size
2Productivity
If conventional fingerprint matching processes are used, then existing systems can operate, but the process is slow and does not scale well to large databases
Solution Approach 1:
The system performs preliminary extraction of mesoscopic features from fingerprint images before the actual matching process. These extracted features are stored and prepared in advance, allowing for rapid comparison and matching operations. This pre-processing step significantly reduces the time required during the actual matching phase, enabling the system to handle large databases efficiently
Solution Approach 2:
The patent changes the fundamental parameters of fingerprint representation by using mesoscopic features extracted through deep learning instead of traditional minutia features. This parameter transformation enables more efficient comparison operations and allows the system to scale to large databases while maintaining high processing speeds
3Adaptability or versatility
If traditional fingerprint recognition methods are used, then the system can function with simple architecture, but past data and expert experiences are not leveraged for improvement
Solution Approach 1:
The system implements a deep learning framework that continuously learns from past fingerprint data and improves its performance over time. The neural network models are trained on extensive datasets and can be further refined through continued use, allowing the system to leverage past data and experiences to enhance matching accuracy. This feedback mechanism enables the system to adapt and improve without requiring increased architectural complexity
Data Source
AI summary
Fingerprint matching may include one or more of the following techniques. Space-frequency representations are adaptively computed for one or more fingerprint images. Key feature points of the fingerprint images are automatically extracted. Mesoscopic features are extracted based on the key feature points and the space-frequency representations. Fingerprint images are matched against a database of known fingerprint images using a matching algorithm based on the key points and mesoscopic features of the fingerprint images. Deep neural networks may be used for some or all of these steps.