The Wasserstein metric or earth mover's distance (EMD) is a useful tool in statistics, machine learning and computer science with many applications to biological or medical imaging, among others.
OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching.
We consider the very challenging task of restoring images (i) which have a large number of missing pixels, (ii) whose existing pixels are corrupted by noise and (iii) the ideal image to be restored contains both cartoon and texture elements.
The first processing step after fingerprint image acquisition is segmentation, i. e. dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region.
In this study we describe a novel attack vector against fingerprint verification systems which we coin skilled impostor attack.
Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e. g. for unlocking mobile phones.
Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images.
Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality.
MHs provide a fixed-length feature vector for a fingerprint which are invariant under rotation and translation.