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International Workshop on Biometric Authentication

BIOMET 2014: Biometric Authentication pp 198–206Cite as

Neural Network and kNN Classifiers for On-Line Signature Verification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8897))

Abstract

In this paper a method for on-line signature verification is presented. The proposed approach consists of the following consecutive steps: feature selection and classification. Experiments are carried out on SUsig database [5] of genuine and forgery signatures of 89 users. The results obtained by applying two different types of classifiers (NN and k-nearest neighbours) are compared. For each user, several NN and kNN models are evaluated by 10-fold cross validation and LOOCV respectively. The “optimal” models are found together with their parameters: number of hidden neurons for NN, type of signature forgeries for training, input features and value of k. The influence of the signature forgery type (random and skilled) over the feature selection and verification is investigated as well.

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Author information

Authors and Affiliations

  1. IICT-BAS, Acad. G. Bonchev St., Block 25A, Sofia, 1113, Bulgaria

    Desislava Boyadzieva & Georgi Gluhchev

Authors
  1. Desislava Boyadzieva
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  2. Georgi Gluhchev
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Corresponding author

Correspondence to Desislava Boyadzieva .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Dimo Dimov

  3. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

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Boyadzieva, D., Gluhchev, G. (2014). Neural Network and kNN Classifiers for On-Line Signature Verification. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-13386-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13385-0

  • Online ISBN: 978-3-319-13386-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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