Abstract
Identity verification based on the dynamic signatures is commonly known issue of biometrics. This process is usually done using methods belonging to one of three approaches: global approach, local function based approach and regional function based approach. In this paper we focus on global features based approach which uses the so called global features extracted from the signatures. We present a new method of global features selection, which are used in the training and classification phase in a context of an individual. Proposed method bases on the evolutionary algorithm. Moreover, in the classification phase we propose a flexible neuro-fuzzy classifier of the Mamdani type. Our method was tested using the SVC2004 public on-line signature database.
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Zalasiński, M., Cpałka, K., Hayashi, Y. (2014). New Method for Dynamic Signature Verification Based on Global Features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_21
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