An algorithm for the evolutionary-fuzzy generation of on-line signature hybrid descriptors

• Autorzy: Zalasiński Marcin , Cpałka Krzysztof , Laskowski Łukasz , Wunsch II Donald C. , Przybyszewski Krzysztof

Identyfikatory

DOI

10.2478/jaiscr-2020-0012

• Języki publikacji: EN

Abstrakty

EN

In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates disproportions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.

Słowa kluczowe

EN

biometrics; on-line signature; dynamic signature; dynamic signature verification; evolutionary-fuzzy signature partitioning; horizontal partitioning; vertical partitioning

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 3
• Strony: 173--187
• Opis fizyczny: Bibliogr. 46 poz., rys.

Twórcy

autor

Zalasiński Marcin

• Czestochowa University of Technology, Department of Computational Intelligence, Częstochowa, Poland

autor

Cpałka Krzysztof

• Czestochowa University of Technology, Department of Computational Intelligence, Częstochowa, Poland

autor

Laskowski Łukasz

• Polish Academy of Sciences, Institute of Nuclear Physics, Krakow, Poland

autor

Wunsch II Donald C.

• Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA

autor

Przybyszewski Krzysztof

• Information Technology Institute, University of Social Sciences, Łódź, Poland and Clark University, Worcester, MA 01610, USA

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).