Interval valued symbolic representation of writer dependent features for online signature verification

This work focusses on exploitation of the notion of writer dependent parameters for online signature verification. Writer dependent parameters namely features, decision threshold and feature dimension have been well exploited for effective verification. For each writer, a subset of the original set of features are selected using different filter based feature selection criteria. This is in contrast to writer independent approaches which work on a common set of features for all writers. Once features for each writer are selected, they are represented in the form of an interval valued symbolic feature vector. Number of features and the decision threshold to be used for each writer during verification are decided based on the equal error rate (EER) estimated with only the signatures considered for training the system. To demonstrate the effectiveness of the proposed approach, extensive experiments are conducted on both MCYT (DB1) and MCYT (DB2) benchmarking online signature datasets consisting of signatures of 100 and 330 individuals respectively using the available 100 global parametric features. © 2017 Elsevier Lt

Advanced methods for two-class problem formulation for on-line signature verification

We present several systems for on-linesignatureverification that approach the problem as a two-class pattern recognition problem. To our knowledge, this is the first work that solves the problem of on-linesignatureverification as a two-classproblem using global (and not local) features. The feature vector obtained by global features is then classified into one of the twoclasses (genuine or impostor) by a support vector machine. Moreover, we show the combination of the systems introduced in this work permit a dramatic reduction of the equal error rate

Advanced methods for two-class problem formulation for on-line signature verification

We present several systems for on-line signature verification that approach the problem as a two-class pattern recognition problem. To our knowledge, this is the first work that solves the problem of on-line signature verification as a two-class problem using global (and not local) features. The feature vector obtained by global features is then classified into one of the two classes (genuine or impostor) by a support vector machine. Moreover, we show the combination of the systems introduced in this work permit a dramatic reduction of the equal error rate

Effectiveness of multifractal analysis for online signature verification

Orientador: Lee Luan LingDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A verificação de identidades de forma confiável é cada vez mais necessária em nossa sociedade amplamente interconectada. Nesse contexto, a verificação biométrica é uma proposta alternativa, e mais segura, aos métodos tradicionalmente utilizados, como senhas e cartões. A análise multifractal, por sua vez, tem sido usada com sucesso em diversas aplicações de processamento de sinais, além disso, diversos estudos mostram a presença de características multifractais em processos naturais. Este trabalho tem como objetivo analisar os sinais referentes às assinaturas dinâmicas, provenientes de equipamentos como PDAs e tablet-pcs, sob o prisma da teoria multifractal. É estudada a capacidade de discriminação da característica multifractal na detecção de falsificações de assinaturas, tanto quando usadas isoladamente quanto em conjunto com características tradicionais, num contexto de fusão de informação, com resultados equivalentes ao estado da arte deste tema. Além disso, é realizada uma quantificação, através da teoria da informação, desta capacidade discriminatória. Por fim, é apresentada uma aplicação alternativa da informação multifractal no contexto da biometria: a análise de qualidade das amostrasAbstract: Reliable identity verification is an increasing necessity in our largely networked society. On this topic, biometric verification is a safer alternative to the traditional methods, such as passwords and ID cards. On the other hand, multifractal analysis has been successfully used in a wide range of signal processing applications; moreover, many works show the occurrence of multifractal traits on biological processes. This work aims at analyzing dynamic signature signals collected through devices such as PDAs and tablet-pcs, from a multifractal perspective. A study of the multifractal features discriminative capabilities on signature forgery detection is realized on two scenarios: when it is the unique feature used by the system, and in tandem with traditional features on an information fusion scheme; with results as good as those found in the state of the art of this area. Furthermore, an information theoretic quantification of the discrimination capability is realized. Finally, an alternative application for such features is presented: the evaluation of samples qualityMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétric