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Online Signature Verification Using Deep Learning Approach

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Abstract

With the advent of advanced technology and its availability at a low price nowadays the biometric verification is getting a common method of person authentication. Online signature recognition is a class of biometric recognition in which a person is identified with the help of its signature. In this paper, a novel deep learning model using shortcut connections has been proposed for online signature recognition. The model has been designed with the modification of the original ResNet model. As the original ResNet model has been designed for the image data so after performing various experiments the modifications in the original ResNet model have been performed that are essential to process the text-based data instead of images. The proposed model has been trained and tested on a custom collected dataset of 4200 online signatures from 280 subjects to achieve an accuracy of 98.6%. It is evident from the results that the proposed model outperforms the state-of-the-art models.

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Acknowledgement

We are thankful to the University of Sindh, Jamshoro, Pakistan, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan for providing research environment and resources and all the volunteers who contributed in the development of the online signature dataset.

Author information

Authors and Affiliations

  1. University of Sindh, Jamshoro, Pakistan

    Mehwish Leghari, Shahzad Memon & Lachhman Das Dhomeja

  2. Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Sindh, Pakistan

    Mehwish Leghari, Akhtar Hussain Jalbani & Asghar Ali Chandio

Authors
  1. Mehwish Leghari

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  2. Shahzad Memon

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  3. Lachhman Das Dhomeja

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  4. Akhtar Hussain Jalbani

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  5. Asghar Ali Chandio

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Corresponding author

Correspondence toMehwish Leghari.

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Lakhmi C. Jain

  3. Faculty of Engineering, Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Marius Mircea Balas

  4. Cankaya University, Ankara, Türkiye

    Dumitru Baleanu

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Leghari, M., Memon, S., Das Dhomeja, L., Jalbani, A.H., Chandio, A.A. (2023). Online Signature Verification Using Deep Learning Approach. In: Balas, V.E., Jain, L.C., Balas, M.M., Baleanu, D. (eds) Soft Computing Applications. SOFA 2020. Advances in Intelligent Systems and Computing, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-031-23636-5_35

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Chapter
JPY 3498
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eBook
JPY 20591
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JPY 25739
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
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