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.

翻译：深度学习在指纹嵌入领域取得了显著成果，该技术对现代自动指纹识别系统至关重要。然而，包括基于CNN和Transformer的方法在内的先前工作未能充分利用指纹中的拓扑结构和相关性等非结构化数据，而这些数据对提升嵌入的可识别性和鲁棒性不可或缺。为解决这一挑战，我们提出了一种名为"基于图神经网络的细节点关系感知模型"（MRA-GNN）的新型指纹嵌入范式。该方法将基于GNN的框架融入指纹嵌入过程，将指纹的拓扑结构和相关性编码为描述性特征，以图嵌入形式实现指纹表示。具体而言，我们将指纹数据及其相对连接重新定义为节点和边，并引入细节点图与指纹图分别表征指纹的拓扑关系和关联结构。我们为MRA-GNN配备了拓扑关系推理模块（TRM）和关联感知模块（CAM），以从这些图中有效学习指纹嵌入。为解决GNN模型中的过平滑问题，我们在提出的模块中集成了前馈模块和图残差连接。实验结果表明，我们的方法在多个指纹数据集上优于现有最优方法，充分验证了该模型在利用指纹非结构化信息方面的有效性。