New Algorithmic Approaches to Point Constellation Recognition

TL;DR

This paper introduces three novel algorithms for point constellation recognition, enhancing fingerprint matching by addressing transformations like rotation, translation, distortion, and occlusion with innovative approaches.

Contribution

It presents three new algorithms, including two generalizations of existing methods and a novel analogy to mechanical system simulation for improved recognition.

Findings

Two algorithms successfully generalize prior methods.

The third algorithm introduces a mechanical analogy approach.

All methods improve robustness to transformations.

Abstract

Point constellation recognition is a common problem with many pattern matching applications. Whilst useful in many contexts, this work is mainly motivated by fingerprint matching. Fingerprints are traditionally modelled as constellations of oriented points called minutiae. The fingerprint verifier's task consists in comparing two point constellations. The compared constellations may differ by rotation and translation or by much more involved transforms such as distortion or occlusion. This paper presents three new constellation matching algorithms. The first two methods generalize an algorithm by Bringer and Despiegel. Our third proposal creates a very interesting analogy between mechanical system simulation and the constellation recognition problem.