MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for Fingerprint Embedding

TL;DR

This paper introduces MRA-GNN, a graph neural network-based fingerprint embedding method that captures topological and correlation information, improving fingerprint recognition robustness and outperforming existing approaches.

Contribution

The paper presents a novel GNN-based framework with modules for topological reasoning and correlation learning, effectively encoding nonstructural fingerprint data.

Findings

Outperforms state-of-the-art fingerprint embedding methods

Effectively encodes topology and correlation in fingerprint data

Improves robustness and accuracy in fingerprint identification

Abstract

Deep learning has achieved remarkable results in fingerprint embedding, which plays a critical role in modern Automated Fingerprint Identification Systems. However, previous works including CNN-based and Transformer-based approaches fail to exploit the nonstructural data, such as topology and correlation in fingerprints, which is essential to facilitate the identifiability and robustness of embedding. To address this challenge, we propose a novel paradigm for fingerprint embedding, called Minutiae Relation-Aware model over Graph Neural Network (MRA-GNN). Our proposed approach incorporates a GNN-based framework in fingerprint embedding to encode the topology and correlation of fingerprints into descriptive features, achieving fingerprint representation in the form of graph embedding. Specifically, we reinterpret fingerprint data and their relative connections as vertices and edges respectively, and introduce a minutia graph and fingerprint graph to represent the topological relations and correlation structures of fingerprints. We equip MRA-GNN with a Topological relation Reasoning Module (TRM) and Correlation-Aware Module (CAM) to learn the fingerprint embedding from these graphs successfully. To tackle the over-smoothing problem in GNN models, we incorporate Feed-Forward Module and graph residual connections into proposed modules. The experimental results demonstrate that our proposed approach outperforms state-of-the-art methods on various fingerprint datasets, indicating the effectiveness of our approach in exploiting nonstructural information of fingerprints.