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

• Autorzy: Saleem Muhammad , Kovari Bence

Identyfikatory

DOI

10.7794/csci.2021.22.4.4102

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) algorithm for online signature verification. The effect of its main parameters is evaluated and used to build an optimized system. The results show that the jk-NN classifier improves the verification accuracy by 0.73–10% as compared to a traditional one-class k-NN classifier. The algorithm achieved reasonable accuracy for different databases: a 3.93% average error rate when using the SVC2004, 2.6% for the MCYT-100, 1.75% for the SigComp’11, and 6% for the SigComp’15 databases. These results followed a state-of-the-art accuracy evaluation where both forged and genuine signatures were used in the training phase. Another scenario is also presented in this paper by using an optimized jk-NN algorithm that 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 rates that were achieved were 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp’15, and 2.22% for SigComp’11.

Słowa kluczowe

EN

k-nearest neighbor; online signature verification; classification

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2021
• Tom: T. 22 (4)
• Strony: 539--551
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Saleem Muhammad

• Budapest University of Technology and Economics, 1111 Budapest, Muegyetem rkp. 3., Hungary

autor

Kovari Bence

• Budapest University of Technology and Economics, 1111 Budapest, Muegyetem rkp. 3., Hungary

Bibliografia

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