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

Sultan Alkaabi, Salman Yussof, Sameera Almulla, Haider Al-Khateeb, Abdulrahman A. Alabdulsalam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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).

Original languageEnglish
Title of host publication2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130125
DOIs
Publication statusPublished - Sep 2019
Event2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019 - Sakhier, Bahrain
Duration: 22 Sep 201923 Sep 2019

Publication series

Name2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019

Conference

Conference2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019
CountryBahrain
CitySakhier
Period22/09/1923/09/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Health Informatics

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  • Cite this

    Alkaabi, S., Yussof, S., Almulla, S., Al-Khateeb, H., & Alabdulsalam, A. A. (2019). A novel architecture to verify offline hand-written signatures using convolutional neural network. In 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019 [8910275] (2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/3ICT.2019.8910275