Letters: An advanced multi-modal method for human authentication featuring biometrics data and tokenised random numbers

Authors:
Alessandra Lumini;Loris Nanni
Affiliations:
DEIS, IEIIT-CNR, Alma Mater Studiorum, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;DEIS, IEIIT-CNR, Alma Mater Studiorum, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Venue:
Neurocomputing
Year:
2006

Citing 7
Cited 2

A simple unpredictable pseudo random number generator

SIAM Journal on Computing
Floating search methods in feature selection

Pattern Recognition Letters
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Thermal Face Recognition Over Time

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Letters: Multihashing, human authentication featuring biometrics data and tokenized random number: A case study FVC2004

Neurocomputing

A multi-modal method based on the competitors of FVC2004 and on palm data combined with tokenised random numbers

Pattern Recognition Letters
PalmHash Code vs. PalmPhasor Code

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this work, we propose a multi-modal method that combines the scores of selected fingerprint matchers with the scores obtained by a Face Authenticator where the facial features are combined with pseudo-random numbers. We propose a novel method to combine the scores of fingerprint matchers based on random subspace Ensemble and we test the method on the systems submitted to FVC2004. Moreover, we show that methods based on tokenised pseudo-random numbers and user specific biometric features are highly dependent upon a parameter, the hashing threshold; we demonstrate that using an ensemble of classifiers it is possible to solve this problem leading to a considerable performance improvement. Finally, we study the fusion among the scores obtained by a Face Authenticator (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004.