Persistent homology machine learning for fingerprint classification

Understand

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.