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Identity verification based on authenticity assessment of a handwritten signature is an important issue in biometrics. There are many effective methods for signature verification taking into account dynamics of a signing process. Methods based on partitioning take a very important place among them. In this paper we propose a new approach to signature partitioning. Its most important feature is the possibility of selecting and processing of hybrid partitions in order to increase a precision of the test signature analysis. Partitions are formed by a combination of vertical and horizontal sections of the signature. Vertical sections correspond to the initial, middle, and final time moments of the signing process. In turn, horizontal sections correspond to the signature areas associated with high and low pen velocity and high and low pen pressure on the surface of a graphics tablet. Our previous research on vertical and horizontal sections of the dynamic signature (created independently) led us to develop the algorithm presented in this paper. Selection of sections, among others, allows us to define the stability of the signing process in the partitions, promoting signature areas of greater stability (and vice versa). In the test of the proposed method two databases were used: public MCYT-100 and paid BioSecure. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a new dynamic signature verification system using vertical partitioning. [•] In our algorithm partitions represent moments of signing process. [•] Our algorithm takes advantage of fuzzy sets and systems theory. [•] Our algorithm allows to extract knowledge about the signature verification. [•] Our algorithm takes into account hierarchy of partitions individually for each user. [Copyright &y& Elsevier]

In this paper, we propose a new class of neuro-fuzzy systems. Moreover, we develop a novel method for reduction of such systems without the deterioration of their accuracy. The reduction algorithm gradually eliminates inputs, rules, antecedents, and the number of discretization points of integrals in the center of area defuzzification method. It then automatically detects and merges similar input and output fuzzy sets. Through computer simulations it is shown that accuracy of the system after reduction and merging has not deteriorated despite the fact that in some cases up to 54% of various parameters and 74% of inputs were eliminated. The reduction algorithm has been tested using well-known classification benchmarks. [ABSTRACT FROM AUTHOR]