Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

Mohammad Saleem; Bence Kovari

doi:10.7494/csci.2021.22.4.4102

Authors

Mohammad Saleem Budapest University of Technology and Economics https://orcid.org/0000-0002-7274-2711
Bence Kovari Budapest University of Technology and Economics https://orcid.org/0000-0003-1555-640X

DOI:

https://doi.org/10.7494/csci.2021.22.4.4102

Keywords:

Signature verification, k-nearest neighbor, classification

Abstract

In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.

The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.

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Author Biographies

Mohammad Saleem, Budapest University of Technology and Economics

Mohammad Saleem is a Ph.D. candidate in software engineering at Budapest University of Technology and Economics, Hungary, at the Department of Automation and Applied Informatics. His research interests include Online signature verification. He worked as a researcher and teacher assistant at Yarmouk University, Irbid, Jordan, and Budapest University of Technology and Economics. He is a member of the Jordanian engineers association.

Bence Kovari, Budapest University of Technology and Economics

Bence Kovari received a Ph.D. degree in software engineering from Budapest University of Technology and Economics, Hungary in 2013. He has been working there since then as a researcher and teacher, currently as an associate professor at the Department of Automation and Applied Informatics. His research interests include software engineering and the automated verification of handwritten signatures. He has over 50 publications in the field. He is a member of the Hungarian Association for Image Processing and Pattern Recognition, a member society of the John von Neumann Computer Society.

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