Topology-Aware Fingerprint Representation Using Graph Convolutional Network for Robust Minutiae Refinements

Most of the fingerprint recognition systems represent minutiae as independent points. This reduces robustness under noise, distortion, and partial impressions. The lack of explicit structural modeling contributes to inconsistent feature reliability, specifically in defocused acquisition conditions. To address these limitations, a Topology-Aware Graph Convolutional Network (Topo-GCN) is proposed. Topo-GCN is a geometric fingerprint representation framework that treats all minutiae points as interrelated nodes and polishes them using a Graph Convolutional Network (GCN). Each node is represented as a 14-dimensional descriptor with the Multi-scale Spatial Feature Tensor (MSFT) technique. A novel Relational Connection Factor (RCF) applies adaptive topology-aware learning to construct edges in place of traditional distance-based graph construction. The Hybrid scoring technique combines learned node representations with graph-theoretic measures to separate genuine minutiae from false ridge information. Furthermore, a lightweight pseudolabeling scheme efficiently trains the model without depending on the large-scale annotated datasets. The proposed Topo-GCN framework achieves an Area under Curve (AUC) of 0.9614, 0.9769, and 0.9831 with consistent verification performance (EER) of 1.85%, 1.39%, and 1.64% and stable relational encoding with mean edge weights of 0.712, 0.739, and 0.758 across FVC2000, FVC2002, and FVC2004 datasets. The results indicate that integrating relational topology into minutiae modeling considerably enhances robustness, making Topo-GCN a viable approach for secure and efficient fingerprint authentication systems.

@article{A2026,
  title     = {Topology-Aware Fingerprint Representation Using Graph Convolutional Network for Robust Minutiae Refinements},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Yoogesh A and Rama Prasath A},
  doi       = {10.14569/IJACSA.2026.0170678},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170678}
}