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Deep learning has achieved remarkable results in fingerprint embedding, whichplays a critical role in modern Automated Fingerprint Identification Systems.However, previous works including CNN-based and Transformer-based approachesfail to exploit the nonstructural data, such as topology and correlation infingerprints, which is essential to facilitate the identifiability androbustness of embedding. To address this challenge, we propose a novel paradigmfor fingerprint embedding, called Minutiae Relation-Aware model over GraphNeural Network (MRA-GNN). Our proposed approach incorporates a GNN-basedframework in fingerprint embedding to encode the topology and correlation offingerprints into descriptive features, achieving fingerprint representation inthe form of graph embedding. Specifically, we reinterpret fingerprint data andtheir relative connections as vertices and edges respectively, and introduce aminutia graph and fingerprint graph to represent the topological relations andcorrelation structures of fingerprints. We equip MRA-GNN with a Topologicalrelation Reasoning Module (TRM) and Correlation-Aware Module (CAM) to learn thefingerprint embedding from these graphs successfully. To tackle theover-smoothing problem in GNN models, we incorporate Feed-Forward Module andgraph residual connections into proposed modules. The experimental resultsdemonstrate that our proposed approach outperforms state-of-the-art methods onvarious fingerprint datasets, indicating the effectiveness of our approach inexploiting nonstructural information of fingerprints.

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