Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms.

Fingerprint minutiae extraction is a critical issue in fingerprint recognition. Both missing and spurious minutiae hinder the posterior matching process. Spurious minutiae are more frequent than missing ones, but they can be removed by post-processing. In this work, we study the usage of a state-of-the-art minutiae extractor, MINDTCT, and we analyze its major drawback: the presence of spurious minutiae lying on the borders of the fingerprint and out its area. In order to overcome this problem, we use two different filtering approaches based on the convex hull of the minutiae and the segmentation of the fingerprint. We will analyze, supported by an exhaustive experimental study, the efficacy of these methods to remove spurious minutiae. We will evaluate both the effect on different state-of-the-art matchers and the goodness of the minutiae, by comparing the extracted minutiae with the ground-truth ones. For this purpose, the experiments have been performed on several databases of both real and synthetic fingerprints. The filters used allow us to remove spurious minutiae, resulting in more accurate results even in the case of robust matchers. The EER is improved up to 2% for good quality databases, and up to 25% for FVC databases. Additionally, the matching time is accelerated, since less minutiae are processed, attaining up to a 60% runtime reduction for the tested database.