Fingerprint recognition with deep learning

Fingerprint matching is a fundamental component of contemporary biometric security; however, accuracy of matching in the presence of noise and realistic environments is still arduous for traditional algorithms. While crucial, fingerprint matching suffers from noisy prints, compromised skin, and extensive databases that make consistent matching difficult. Here, we propose a fully reproducible, end-to-end workflow that integrates traditional fingerprint science with a state-of-the-art Siamese neural network to solve these issues. The mechanism is composed of three linked Python programs. The first one is trained over a batch of 500 real images with contrastive loss to acquire 128-dimensional embeddings that aim to reduce intra-subject distance and increase inter-subject separation. A calibration component then monitors the distributions of real and imposter distances, calculates some candidate thresholds, and computes the optimal Equal Error Rate (EER) operating point best balancing security and convenience. Third, a graphical desktop user interface loads the calibrated encoder, pre-computes a searchable embedding database, and offers one-to-many real-time identification with three-dimensional PCA visualization that accentuates the query print and its closest neighbor. Experimental analysis demonstrates that the calibrated system obtains low EER on the development set and preserves sub-second inference on consumer hardware, rendering it practical for forensic triage, border control kiosks, and mobile authentication. Through the unification of training, calibration, and deployment pipelines within a single, well-documented codebase, the project provides a valuable foundation for researchers and engineers to achieve advances in deep metric learning for production fingerprint recognition systems. In order to facilitate future research endeavors, all scripts, trained weights, and calibration logs are made available under an open-source license, thereby enabling replication, benchmarking, and easy adaptation for large public datasets.