Abstract
Dynamic signature is a biometric attribute which is commonly used for identity verification. Artificial intelligence methods, especially population-based algorithms (PBAs), can be very useful in the dynamic signature verification process. They are able to, among others, support selection of the most characteristic descriptors of the signature or perform signature partitioning. In this paper, we focus on creating the most characteristic signature partitions using different PBAs and comparing their effectiveness. The simulations whose results are presented in this paper were performed using the BioSecure DS2 database distributed by the BioSecure Association.
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Acknowledgment
This paper was financed under the program of the Minister of Science and Higher Education under the name ’Regional Initiative of Excellence’ in the years 2019–2022, project number 020/RID/2018/19 with the amount of financing PLN 12 000 000.
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Zalasiński, M., Cpałka, K., Niksa-Rynkiewicz, T., Hayashi, Y. (2020). Signature Partitioning Using Selected Population-Based Algorithms. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_44
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