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

Authors:
Loris Nanni;Alessandra Lumini
Affiliations:
DEIS, IEIIT - CNR, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy;DEIS, IEIIT - CNR, Universití di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Venue:
Pattern Recognition Letters
Year:
2008

Citing 12
Cited 0

Floating search methods in feature selection

Pattern Recognition Letters
The Random Subspace Method for Constructing Decision Forests

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

Handbook of Fingerprint Recognition
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Fingerprint Verification Systems

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved BioHashing for human authentication

Pattern Recognition
An analysis of BioHashing and its variants

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

Neurocomputing
Combining multiple matchers for fingerprint verification: a case study in FVC2004

ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Personal identification using knuckleprint

SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this work, we propose a multi-modal method that combines the scores of selected fingerprint matchers with the score obtained by a palm authenticator where the palm features are combined with pseudo-random numbers. We use a random subspace of AdaBoost.M1 to combine the scores of the best fingerprint matchers automatically selected among the competitors of FVC2004, and we study the performance when a different number of competitors are involved in the fusion. Moreover a deep study is carried out to design a hybrid system based on the combination of palm image features and a personal key. In conclusion, the aim of this work is to design a multi-modal biometric system and to analyze the benefits and the limits of fusion approaches in order to boost the performance of hybrid system in the worst testing hypothesis when an ''impostor'' steal the personal key of the user A before he tries to authenticate as A. The experimental results reported in this paper confirm that using a multi-modal ensemble of matchers it is possible to overcome some of the limitations of each single matcher leading to a considerable performance improvement.