Neural approaches for human signature verification

Author

Lee, Luan Ling

Author_Institution

Univ. Estadual de Campinas, Sao Paulo, Brazil

Volume

2

fYear

1995

fDate

14-16 Aug 1995

Firstpage

1055

Abstract

This paper describes three neural network (NN) based approaches for on-line human signature verification: Bayes multilayer perceptrons (BMP), time-delay neural networks (TDNN), input-oriented neural networks (IONN). The backpropagation algorithm was used for the network training. A signature is input as a sequence of instantaneous absolute velocity (|v(t)|) extracted from a pair of spatial coordinate time functions (x(t), y(t)). The BMP provides the lowest misclassification error rate among three types of networks

Keywords

backpropagation; delays; handwriting recognition; multilayer perceptrons; neural nets; Bayes multilayer perceptrons; backpropagation algorithm; human signature verification; input-oriented neural networks; instantaneous absolute velocity; misclassification error rate; network training; neural approaches; spatial coordinate time functions; time-delay neural networks; Backpropagation algorithms; Error analysis; Forgery; Handwriting recognition; Humans; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern recognition; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

0-8186-7128-9

Type

conf

DOI

10.1109/ICDAR.1995.602087

Filename

602087