Comparison of ART-2 and SOFM Based Neural Network Verifiers

P. Mautner, V. Matousek, T. Marsalek, O. Rohlik

2004

Abstract

The Carpenter-Grosberg ART-2 and Kohonen Self-organizing Feature Map (SOFM) have been developed for the clustering of input vectors and have been commonly used as unsupervised learned classiers. In this paper we describe the use of these neural network models for signature verification. The biometric data of all signatures were acquired by a special digital data acquisition pen and fast wavelet transformation was used for feature extraction. The part of genuine signature data was used for training both signature verifiers. The architecture of the veriers and achieved results are discussed here and ideas for future research are also suggested.

References

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Paper Citation


in Harvard Style

Mautner P., Matousek V., Marsalek T. and Rohlik O. (2004). Comparison of ART-2 and SOFM Based Neural Network Verifiers . In Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004) ISBN 972-8865-14-7, pages 41-48. DOI: 10.5220/0001149300410048


in Bibtex Style

@conference{anns04,
author={P. Mautner and V. Matousek and T. Marsalek and O. Rohlik},
title={Comparison of ART-2 and SOFM Based Neural Network Verifiers},
booktitle={Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004)},
year={2004},
pages={41-48},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001149300410048},
isbn={972-8865-14-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004)
TI - Comparison of ART-2 and SOFM Based Neural Network Verifiers
SN - 972-8865-14-7
AU - Mautner P.
AU - Matousek V.
AU - Marsalek T.
AU - Rohlik O.
PY - 2004
SP - 41
EP - 48
DO - 10.5220/0001149300410048