Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach

Author:

Zalasiński Marcin1ORCID,Laskowski Łukasz2ORCID,Niksa-Rynkiewicz Tacjana3ORCID,Cpałka Krzysztof1ORCID,Byrski Aleksander4ORCID,Przybyszewski Krzysztof5ORCID,Trippner Paweł6,Dong Shi7ORCID

Affiliation:

1. Częstochowa University of Technology , Department of Intelligent Computer Systems , Al. Armii Krajowej 36 , Częstochowa , Poland

2. Polish Academy of Sciences, Institute of Nuclear Physics , Kraków , Poland

3. Gdańsk University of Technology , Faculty of Ocean Engineering and Ship Technology , 11/12 Gabriela Narutowicza Street , Gdańsk , Poland

4. AGH University of Science and Technology , Institute of Computer Science , Kraków , Poland

5. University of Social Sciences , Institute of Information Technologies , 9 Sienkiewicza Street , Łódź , Poland

6. University of Social Science , Management Department , 9 Sienkiewicza Street, 90–113 Łódź , Poland

7. Zhoukou Normal University , School of Computer Science and Technology , China

Abstract

Abstract In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modeling and Simulation,Information Systems

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