Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model
1 Department of Applied Mathematics, University of Stellenbosch, Matieland 7602, South Africa
2 Department of Electrical and Electronic Engineering, University of Stellenbosch, Matieland 7602, South Africa
EURASIP Journal on Advances in Signal Processing 2004, 2004:925026 doi:10.1155/S1110865704309042Published: 21 April 2004
We developed a system that automatically authenticates offline 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 offline. 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.