Neural approaches for human signature verification

Authors:
Luan Ling Lee
Affiliations:
-
Venue:
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Year:
1995

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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.