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Multi-Layer Perceptron Neural Network for an Offline Signature Verification System

Authors: Nuhu AS, Adam N, Gadam AM, Dajab DD

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Signature verification using neural networks is characterized by the use of pre-processing techniques such as normalization, morphological operations and median filtering. In this work, an effective method for offline signature verification system based on multi-layer perceptron (MLP) was proposed. A signature can be divided into five logically connected, basic aspects or layers which are learnt by a single set of weights. The system was built based on a four-hidden layer neural network. An accuracy of 82.5% was attained in recognizing genuine and forged signatures which outperformed the state-of-the-art techniques that incorporate feature selection and preprocessing operations.

Affiliations: Department of Electrical and Electronics Engineering, University of Jos, Plateau State, Nigeria.
Keywords: Feature Extraction, Segmentation, Image Processing, Multi-perceptron, Recognition
Published date: 2021/06/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Chemical Engineering Department, Faculty of Engineering, University of Benin, PMB 1154, Ugbowo, Benin City, Edo State, Nigeria.