  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.
