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A novel architecture to verify offline hand-written signatures using convolutional neural network

Authentication; Convolution; Neural networks; Verification; Convolutional neural network; Data augmentation; Forensics; Handwritten signatures; Signature verification; Network architecture

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Bibliographic Details
Main Authors: Alkaabi S., Yussof S., Almulla S., Al-Khateeb H., Alabdulsalam A.A.
Other Authors: 57212311690
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-24468
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spelling my.uniten.dspace-244682023-05-29T15:23:46Z A novel architecture to verify offline hand-written signatures using convolutional neural network Alkaabi S. Yussof S. Almulla S. Al-Khateeb H. Alabdulsalam A.A. 57212311690 16023225600 36473139200 55339456900 57219701686 Authentication; Convolution; Neural networks; Verification; Convolutional neural network; Data augmentation; Forensics; Handwritten signatures; Signature verification; Network architecture Hand-written signatures are marked on documents to establish legally binding evidence of identity and intent. However, they are prone to forgery, and the design of an accurate feature extractor to distinguish between highly-skilled forgeries and genuine signatures is a challenging task. In this paper, we propose a Convolution Neural Network (CNN) architecture for Signature Verification (SV). The algorithm is trained using two signatures, genuine and forged. Then the SV module performs a classification task to determine if any two signatures are of the same individual or not. The simulation results show that the proposed method can achieve 27% (relatively) better results than the benchmark scheme. The paper also integrated different data augmentation techniques for the signature data, which further improved the efficiency of the proposed method by 14% (relative). � 2019 IEEE. Final 2023-05-29T07:23:46Z 2023-05-29T07:23:46Z 2019 Conference Paper 10.1109/3ICT.2019.8910275 2-s2.0-85076434875 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076434875&doi=10.1109%2f3ICT.2019.8910275&partnerID=40&md5=239e1a66de5aaae0d64d6436ce7011a5 https://irepository.uniten.edu.my/handle/123456789/24468 8910275 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Authentication; Convolution; Neural networks; Verification; Convolutional neural network; Data augmentation; Forensics; Handwritten signatures; Signature verification; Network architecture
author2 57212311690
author_facet 57212311690
Alkaabi S.
Yussof S.
Almulla S.
Al-Khateeb H.
Alabdulsalam A.A.
format Conference Paper
author Alkaabi S.
Yussof S.
Almulla S.
Al-Khateeb H.
Alabdulsalam A.A.
spellingShingle Alkaabi S.
Yussof S.
Almulla S.
Al-Khateeb H.
Alabdulsalam A.A.
A novel architecture to verify offline hand-written signatures using convolutional neural network
author_sort Alkaabi S.
title A novel architecture to verify offline hand-written signatures using convolutional neural network
title_short A novel architecture to verify offline hand-written signatures using convolutional neural network
title_full A novel architecture to verify offline hand-written signatures using convolutional neural network
title_fullStr A novel architecture to verify offline hand-written signatures using convolutional neural network
title_full_unstemmed A novel architecture to verify offline hand-written signatures using convolutional neural network
title_sort novel architecture to verify offline hand-written signatures using convolutional neural network
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428294640304128
score 13.214268

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