Joshua J. Engelsma

TL;DR: Using Infant-Prints, this work has collected a longitudinal database of infant fingerprints and demonstrated its ability to perform accurate and reliable recognition of infants enrolled at the ages 0-3 months, in time for effective delivery of critical vaccinations and nutritional supplements.

TL;DR: In this paper, a joint model for spoof detection and matching was proposed to simultaneously perform both tasks without compromising the accuracy of either task, achieving an authentication accuracy of TAR = 100% @ FAR = 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on LiveDet 2015 dataset.

TL;DR: Results of a controlled, white-box evaluation of one open-source and two commercial-off-the-shelf minutiae-based matchers in terms of their robustness against controlled perturbations introduced into the inputMinutiae feature sets reveal that the performance of fingerprintminutiae matchers are more susceptible to non-linear distortion and missing minutAE than spurious minutae and small positional displacements of the minutia locations.

TL;DR: In this paper, the identity loss is incorporated to guide the generator to synthesize fingerprints corresponding to more distinct identities and the characteristics of the synthesized fingerprints are shown to be more similar to real fingerprints than real fingerprints.

TL;DR: This work proposes the first use of a Vision Transformer (ViT) to learn a discriminative minutiae matching embedding and shows that by fusing embeddings learned by CNNs and ViTs the authors can reach near parity with a commercial state-of-the-art (SOTA) matcher.