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Mediterranean Conference on Pattern Recognition and Artificial Intelligence

MedPRAI 2020: Pattern Recognition and Artificial Intelligence pp 242–254Cite as

Offline Signature Verification Using Feature Learning and One-Class Classification

Part of the Communications in Computer and Information Science book series (CCIS,volume 1322)

Abstract

Offline signature verification remains the most commonly employed authentication modality and enjoys global acceptance. From the view point of computerized verification, concluding the authenticity of a signature offers a challenging problem for the pattern classification community. A major proportion of computerized solutions treat signature verification as a two-class classification problem where both genuine and forged signatures are employed for training purposes. For most of the real world scenarios however, only genuine signatures of individuals are available. This paper presents a signature verification technique that relies only on genuine signature samples. More precisely, we employ convolutional neural networks for learning effective feature representations and a one-class support vector machine that learns the genuine signature class for each individual. Experiments are carried out in a writer-dependent as well as writer-independent mode and low error rates are reported by only employing genuine signatures in the training sets.

Keywords

  • Feature learning
  • One-class classification
  • Signature verification
  • Forgery detection

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Fig. 1.

Notes

  1. 1.

    Download Dataset:http://www.cedar.buffalo.edu/NIJ/data/signatures.rar.

  2. 2.

    Download Dataset: http://tc11.cvc.uab.es/datasets/SigWIcomp2015_1.

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Author information

Authors and Affiliations

  1. Bahria University, Islamabad, Pakistan

    Safia Shabbir & Imran Siddiqi

  2. National University of Sciences and Technology, Islamabad, Pakistan

    Muhammad Imran Malik

Authors
  1. Safia Shabbir
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  2. Muhammad Imran Malik
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  3. Imran Siddiqi
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Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Digital Research Center of Sfax, Sfax, Tunisia

    Yousri Kessentini

  3. Bahria University, Islamabad, Pakistan

    Prof. Imran Siddiqi

  4. Digital Research Centre of Sfax, Sfax, Tunisia

    Prof. Mohamed Jmaiel

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Shabbir, S., Malik, M.I., Siddiqi, I. (2021). Offline Signature Verification Using Feature Learning and One-Class Classification. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_18

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  • DOI: https://doi.org/10.1007/978-3-030-71804-6_18

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