Fingerprint Verification and Classification Using a Siamese Neural Network with Real-Time Interface Integration

Abstract

 Fingerprints can be interpreted as maps that show local features like minutiae. Fingerprint images also feature ridge flows that look just like contour lines, which are typically employed to show the latitude of region on a two-dimensional map. The cores and deltas are one of the sites that ridge flows focus on. The area around a core point is more like a mountain, while the area around a delta point is more like where two rivers meet. Fingerprint recognition has long been a basic biometric method for identifying people since it is distinctive, stable, and reliable. But standard fingerprint verification systems generally have trouble with changes in image quality, aberrations, and prints that aren't complete. This study suggests a deep learning approach that uses a Siamese Neural Network architecture made for verifying fingerprints. The model is trained on a dataset that include both synthetic and real fingerprint images of different levels of difficulty. A unique data generator creates matching and non-matching fingerprint pairs on the fly using real-time augmentation algorithms. The network learns a similarity function that can tell if two fingerprint images belong to the same person. The model is quite accurate, precise, and robust when tested on many different sets of data. Gradio is also used to develop a real-time user interface that lets users upload fingerprint pairs and get verification results right away.

This research demonstrates the advantages of deep metric learning in biometrics and lays a strong basis for future progress in secure, scalable, and interactive fingerprint recognition systems.