Attention-Driven Fingerprint Network for Latent-to-Rolled Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fingerprint recognition systems face challenges in accurately matching partial overlap and cross-sensor interoperability, particularly with latent fingerprints, and are computationally demanding, with methods lacking a combination of global and local embeddings for high accuracy and throughput.
Innovation Solution
An attention-driven fingerprint recognition network (AFR-Net) that integrates CNN and attention-based models, using a shared feature extraction and parallel classification layers, with a multi-stage matching process involving global and local embeddings, and a spatial alignment module to enhance and align fingerprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CNN-based models are used for fingerprint recognition, then recognition accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the fingerprint recognition task into two distinct components: global embedding extraction using lightweight CNNs for overall pattern matching, and local minutiae extraction for detailed feature analysis. This segmentation allows each component to use appropriately optimized algorithms, reducing overall computational complexity while maintaining high recognition accuracy through the fusion of both approaches.
2Measurement precision
If local embeddings (minutiae) are used to improve robustness to partial occlusions, then matching accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent implements a two-stage matching process where global embeddings are first used to quickly filter and identify candidate matches, and then local minutiae are applied only to these candidates for final verification. This partial application of computationally expensive local feature analysis to only necessary cases maintains high matching accuracy while significantly improving overall processing speed compared to analyzing all pairs with minutiae.
3Measurement precision
If traditional enhancement methods are applied to latent fingerprints, then recognition performance is improved, but computational demand increases
Solution Approach 1:
The patent incorporates a lightweight enhancement module that performs preliminary processing on latent fingerprint images before they enter the main recognition pipeline. This preliminary enhancement focuses computational resources on critical degradation removal tasks, improving subsequent recognition performance while keeping the overall computational demand manageable by avoiding redundant processing in later stages.
4Measurement precision
If ViT architecture is used for fingerprint recognition, then representation learning is improved, but training data requirements increase
Solution Approach 1:
The patent merges the strengths of CNN-based global embedding extraction with ViT-based attention mechanisms in a hybrid architecture. The CNN component efficiently extracts global patterns with minimal data requirements, while the attention mechanism enhances local feature relationships. This combination achieves improved representation learning without requiring the extensive training data that a pure ViT architecture would demand.
Data Source
AI summary
A method and system for fingerprint recognition includes generating a first set of feature embeddings for a first image at a feature extractor, generating a second set of feature embeddings for a second image at the feature extractor, generating a third set of embeddings at a first classification head from the first set of feature embeddings, generating a fourth set of embeddings at a second classification head from the first set of feature embeddings, generating a fifth set of embeddings at the first classification head from the second, set of feature embeddings, generating a sixth set of embeddings at the second classification head from the second set of feature embeddings, generating a first similarity score based on the third and fourth sets of embeddings and the fifth and sixth set of embeddings and generating a similarity indicator based on the first similarity score.