MULTIPLE CLASSIFIERS ERROR RATE OPTIMIZATION APPROACHES OF AN AUTOMATIC SIGNATURE VERIFICATION (ASV) SYSTEM

Sharifah M. Syed Ahmad

Abstract

Decision level management is a crucial aspect in an Automatic Signature Verification (ASV) system, due to its nature as the centre of decision making that decides on the validity or otherwise of an input signature sample. Here, investigations are carried out in order to improve the performance of an ASV system by applying multiple classifier approaches, where features of the system are grouped into two different sub- sets, namely static and dynamic subsets, hence having two different classifiers. In this work, three decision fusion methods, namely Majority Voting, Borda Count and cascaded multi-stage cascaded classifiers are analyzed for their effectiveness in improving the error rate performance of the ASV system. The performance analysis is based upon a database that reflects an actual user population in a real application environment, where as the system performance improvement is calculated with respect to the initial system Equal Error Rate (EER) where multiple classifiers approaches were not adopted.

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Paper Citation


in Harvard Style

M. Syed Ahmad S. (2007). MULTIPLE CLASSIFIERS ERROR RATE OPTIMIZATION APPROACHES OF AN AUTOMATIC SIGNATURE VERIFICATION (ASV) SYSTEM . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 257-263. DOI: 10.5220/0002050902570263


in Bibtex Style

@conference{visapp07,
author={Sharifah M. Syed Ahmad},
title={MULTIPLE CLASSIFIERS ERROR RATE OPTIMIZATION APPROACHES OF AN AUTOMATIC SIGNATURE VERIFICATION (ASV) SYSTEM},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={257-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002050902570263},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - MULTIPLE CLASSIFIERS ERROR RATE OPTIMIZATION APPROACHES OF AN AUTOMATIC SIGNATURE VERIFICATION (ASV) SYSTEM
SN - 978-972-8865-74-0
AU - M. Syed Ahmad S.
PY - 2007
SP - 257
EP - 263
DO - 10.5220/0002050902570263