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Discriminative Features Mining for Offline Handwritten Signature Verification

  • 3DR Express
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3D Research

Abstract

Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature’s characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, Johor bahru, Johor, Malaysia

    Karrar Neamah & Dzulkifli Mohamad

  2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia

    Tanzila Saba

  3. MIS Department College of Business Administration, Salman Bin Abdul Aziz University, Alkharj, Kingdom of Saudi Arabia

    Amjad Rehman

Authors
  1. Karrar Neamah
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  2. Dzulkifli Mohamad
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  3. Tanzila Saba
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  4. Amjad Rehman
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Corresponding author

Correspondence to Tanzila Saba.

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Neamah, K., Mohamad, D., Saba, T. et al. Discriminative Features Mining for Offline Handwritten Signature Verification. 3D Res 5, 2 (2014). https://doi.org/10.1007/s13319-013-0002-3

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  • DOI: https://doi.org/10.1007/s13319-013-0002-3

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