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Wyszukujesz frazę "signature verification" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
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
Powiązania:
https://bibliotekanauki.pl/articles/1837539.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
biometrics
on-line signature
dynamic signature
dynamic signature verification
evolutionary-fuzzy signature partitioning
horizontal partitioning
vertical partitioning
Opis:
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.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 173-187
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary algorithm for selecting dynamic signatures partitioning approach
Autorzy:
Zalasiński, Marcin
Laskowski, Łukasz
Niksa-Rynkiewicz, Tacjana
Cpałka, Krzysztof
Byrski, Aleksander
Przybyszewski, Krzysztof
Trippner, Paweł
Dong, Shi
Powiązania:
https://bibliotekanauki.pl/articles/2147146.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
identity verification
dynamic signature
hybrid partitions
partitions’ selection
evolutionary algorithm
Opis:
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.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 267--279
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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