Title: Handwritten signatures recognition using Liquid State Machine

Authors: Souaad Belhia, Mohamed Benyettou, Ahmed Lehireche

Addresses: Computer Department, University Djillali Liabes, Sidi bel-Abbes, Algeria. ' University of Science and Technologies, Mohamed Boudiaf-Oran, Algeria. ' Computer Department, University Djillali Liabes, Sidi bel-Abbes, Algeria

Abstract: In this work, we checked the possibility of using Biological Neural Networks to analyse the complex multi-dimensional features for purposes of data recognition and classification. We investigated a recently proposed model |Liquid State Machine (LSM) using spiking neural network| and its applicability for recognition of the |handwritten signature problem|. This project includes complex data analysis, adaptation of visual features to the neural microcircuit and comparison of the results with baseline algorithms. Experiments show that 90% correct classification rate can be achieved on a database of over 8000 signature images.

Keywords: LSMs; liquid state machines; spiking neural networks; signature recognition; handwritten signatures; data recognition; data classification.

DOI: 10.1504/IJBM.2011.039416

International Journal of Biometrics, 2011 Vol.3 No.2, pp.148 - 158

Published online: 24 Jan 2015 *

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