Abstract
Methods using dynamic signature for identity verification may be divided into three main categories: global methods, local function based methods and regional function based methods. Global methods base on a set of global parametric features, which are extracted from signature of user. Global feature extraction methods have been often presented in the literature. Another interesting task is selection of a features group which will be considered individually for each user during training and verification process. In this paper we propose a new approach to automatic evolutionary selection of the dynamic signature global features. Our method was tested with use of the SVC2004 public on-line signature database.
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Zalasiński, M., Łapa, K., Cpałka, K. (2013). New Algorithm for Evolutionary Selection of the Dynamic Signature Global Features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38610-7_11
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DOI: https://doi.org/10.1007/978-3-642-38610-7_11
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