Welcome to BioLab 26/09/2017 14:33


The way a person signs her/his name is known to be a characteristic of that individual. Although signatures require contact and effort with the writing instrument, they are considered acceptable in many government, legal, and commercial transactions as a method of personal authentication. Signatures are a behavioral biometric, evolve over a period of time and are influenced by physical and emotional conditions of the signatories: this makes signature recognition a very challenging biometric recognition problem.

In on-line signature verification, the time functions of the dynamic signing process (e.g., position trajectories, or pressure versus time) are available for recognition. The main approaches proposed in the literature to extract relevant information from on-line signature data are: i) feature-based approaches, in which a holistic vector representation consisting of global features is derived from the acquired signature trajectories, and ii) function-based approaches, where time sequences describing local properties of the signature are used for recognition (e.g., position trajectory, velocity,acceleration, force, or pressure). One major research trend in on-line signature verification is the combination of systems that utilize the different information levels in the form of similarity scores.

The Biometric System Laboratory proposed some approaches for signature verification based on an ensemble of one-class classifiers, where a classifier is trained for each individual without any knowledge of the other users.

(Click here if you are interested in any of the publications below)

L. Nanni, E. Maiorana, A. Lumini and P. Campisi, "On-Line Signature Verification: Comparison and Fusion of Feature Based and Function Based Classifiers", in Harvey Schuster and Wilfred Metzger, Biometrics: Methods, Applications and Analyses, Nova Publishers, 2010. Abstract

L. Nanni, E. Maiorana, A. Lumini and P. Campisi, "Combining local, regional and global matchers for a template protected on-line signature verification system", Expert Systems With Applications, vol.37, no.5, pp.3676-3684, May 2010. Abstract

L. Nanni and A. Lumini, "Ensemble of On-line signature matchers based on OverComplete Feature generation", Expert Systems With Applications, vol.36, no.3, pp.5291-5296, April 2009. Abstract

A. Lumini and L. Nanni, "Over-complete feature generation and feature selection for biometry", Expert Systems With Applications, vol.35, no.4, pp.2049-2055, November 2008. Abstract

L. Nanni and A. Lumini, "A novel local on-line signature verification system", Pattern Recognition Letters, vol.29, no.5, pp.559-568, April 2008. Abstract

L. Nanni, "An advanced multi-matcher method for on-line signature verification featuring global features and tokenised random numbers", NeuroComputing, vol.69, no.16, pp.2402-2406, October 2006. Abstract

L. Nanni, "Experimental comparison of one-class classifiers for on-line signature verification", NeuroComputing, vol.69, no.7, pp.869-873, March 2006. Abstract

L. Nanni and A. Lumini, "Human authentication featuring signatures and tokenised random number", NeuroComputing, vol.69, no.7-9, pp.858-861, March 2006. Abstract

L. Nanni and A. Lumini, "Advanced methods for two-class problem formulation for on-line signature verification", NeuroComputing, vol.69, no.7-9, pp.854-857, March 2006. Abstract

L. Nanni and A. Lumini, "Ensemble of Parzen Window Classifiers for on-line signature verification", NeuroComputing, vol.68, no.6, pp.217-224, October 2005. Abstract

J. Fierrez-Aguilar, L. Nanni, J. Lopez-Penalba, J. Ortega-Garcia and D. Maltoni, "An On-Line Signature Verification System Based on Fusion of Local and Global Information", in proceedings Audio- and Video-based Biometric Person Authentication, New York, July 2005. Abstract

Copyright © 2017 Biometric System Laboratory