Persistent homology machine learning for fingerprint classification

TL;DR

This paper demonstrates that topological data analysis applied to minutiae points and fingerprint images can achieve high accuracy in fingerprint classification, challenging previous assumptions about the usefulness of minutiae for this task.

Contribution

It introduces a novel application of persistent homology to fingerprint classification, combining topological features from minutiae and images for improved accuracy.

Findings

High classification accuracy using TDA on minutiae points.

Combining TDA features from minutiae and images outperforms individual methods.

Effective feature selection on barcodes explored for machine learning.

Abstract

The fingerprint classification problem is to sort fingerprints into pre-determined groups, such as arch, loop, and whorl. It was asserted in the literature that minutiae points, which are commonly used for fingerprint matching, are not useful for classification. We show that, to the contrary, near state-of-the-art classification accuracy rates can be achieved when applying topological data analysis (TDA) to 3-dimensional point clouds of oriented minutiae points. We also apply TDA to fingerprint ink-roll images, which yields a lower accuracy rate but still shows promise, particularly since the only preprocessing is cropping; moreover, combining the two approaches outperforms each one individually. These methods use supervised learning applied to persistent homology and allow us to explore feature selection on barcodes, an important topic at the interface between TDA and machine learning. We test our classification algorithms on the NIST fingerprint database SD-27.