Signature verification (SV) toolbox: Application of PSO-NN

Authors:
M. Taylan Das;L. Canan Dulger
Affiliations:
Gaziantep University, Mechanical Engineering Department, 27310 Gaziantep, Turkiye;Gaziantep University, Mechanical Engineering Department, 27310 Gaziantep, Turkiye
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

Analysis of signature is a widely used and developed area of research for personal verification. A typical signature verification (SV) system generally consists of four components: data acquisition, pre-processing, feature extraction and verification. A reliable SV toolbox, based on the verification of off-line signatures is developed with the proposed algorithm. The technique is based on a neural network (NN) approach trained with particle swarm optimization (PSO) algorithm. To test the performance of the proposed PSO-NN algorithm two types of forgeries-unskilled and skilled-are examined. The experimental results are illustrated on the selected signature databases and presented herein.