Offline signature verification using the discrete radon transform and a hidden Markov model

Authors:
J. Coetzer;B. M. Herbst;J. A. du Preez
Affiliations:
Department of Applied Mathematics, University of Stellenbosch, Matieland, South Africa;Department of Applied Mathematics, University of Stellenbosch, Matieland, South Africa;Department of Electrical and Electronic Engineering, University of Stellenbosch, Matieland, South Africa
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

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Abstract

We developed a system that automatically authenticates offine handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offine. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.