Eigen-Model Projections for Protected On-line Signature Recognition

2011 ◽  
pp. 49-60
Author(s):  
Emanuele Maiorana ◽  
Enrique Argones Rúa ◽  
Jose Luis Alba Castro ◽  
Patrizio Campisi
Keyword(s):  
On Line ◽  
Signature Recognition ◽  
Eigen Model

Cancellable Biometrics for On-line Signature Recognition

2011 ◽  
pp. 290-315 ◽  
Author(s):  
Emanuele Maiorana ◽  
Patrizio Campisi ◽  
Alessandro Neri
Keyword(s):  
Recognition System ◽  
Design Of Algorithms ◽  
Biometric Data ◽  
The Public ◽  
Protection Scheme ◽  
Protection Method ◽  
Signature Database ◽  
On Line ◽  
Signature Recognition ◽  
Recognition Systems

With the widespread diffusion of biometrics-based recognition systems, there is an increasing awareness of the risks associated with the use of biometric data. Significant efforts are therefore being dedicated to the design of algorithms and architectures able to secure the biometric characteristics, and to guarantee the necessary privacy to their owners. In this work we discuss a protected on-line signature-based biometric recognition system, where the considered biometrics are secured by applying a set of non-invertible transformations, thus generating modified templates from which retrieving the original information is computationally as hard as random guessing it. The advantages of using a protection method based on non-invertible transforms are exploited by presenting three different strategies for the matching of the transformed templates, and by proposing a multi-biometrics approach based on score-level fusion to improve the performances of the considered system. The reported experimental results, evaluated on the public MCYT signature database, show that the achievable recognition rates are only slightly affected by the proposed protection scheme, which is able to guarantee the desired security and renewability for the considered biometrics.

On-line signature recognition based on VQ-DTW

2007 ◽  
Vol 40 (3) ◽  
pp. 981-992 ◽  
Author(s):  
Marcos Faundez-Zanuy
Keyword(s):  
On Line ◽  
Signature Recognition

On-line Signature Recognition using Neural Network

2006 ◽  
Vol 06 (3) ◽  
pp. 55-65
Author(s):  
Jitti Boonkueng ◽  
Dr.Pusadee Seresangtakul
Keyword(s):  
Neural Network ◽  
On Line ◽  
Signature Recognition

Cancelable Templates for Sequence-Based Biometrics with Application to On-line Signature Recognition

2010 ◽  
Vol 40 (3) ◽  
pp. 525-538 ◽  
Author(s):  
Emanuele Maiorana ◽  
Patrizio Campisi ◽  
Julian Fierrez ◽  
Javier Ortega-Garcia ◽  
Alessandro Neri
Keyword(s):  
On Line ◽  
Signature Recognition

scholarly journals A Combined Method for On-Line Signature Verification

2014 ◽  
Vol 14 (2) ◽  
pp. 92-97
Author(s):  
Desislava Boyadzhieva ◽  
Georgi Gluhchev
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Signature Verification ◽  
System Evaluation ◽  
Combined Method ◽  
Average Accuracy ◽  
Feature Extraction And Selection ◽  
On Line ◽  
Signature Recognition

Abstract A combined method for on-line signature verification is presented in this paper. Moreover, all the necessary steps in developing a signature recognition system are described: signature data pre-processing, feature extraction and selection, verification and system evaluation. NNs are used for verification. The influence of the signature forgery type (random and skilled) over the verification results is investigated as well. The experiments are carried out on SUsig database which consists of genuine and forgery signatures of 89 users. The average accuracy is 98.46%.

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