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A mathematical model of changes of parameters of handwritten patterns depending on the signers states

Published under licence by IOP Publishing Ltd
, , Citation A E Samotuga 2018 J. Phys.: Conf. Ser. 1050 012076 DOI 10.1088/1742-6596/1050/1/012076

1742-6596/1050/1/012076

Abstract

Authors of some studies point out the need of taking into account the state of employee to reduce the risk that he will cause damage to organization including information resources. An assessment of psychophysiological state is possible on the basis of the dynamic parameters of a signature. Etalons creation of handwritten signature for each state significantly complicates the formation of an etalons base of subjects. The purpose of the study is to provide a mechanism for the formation of subjects etalons in different states without the need to collect of handwritten signature in each states. To achieve the goal we should solve the problem of finding the regularity of the signature parameters changes while changing the states of signers. We develop a model of changing signature parameters depending on the state based on the discovered regularities. We compared the accuracy of the obtained model by estimating the probabilities of erroneous decisions obtained in the experiment of states recognition using etalons obtained in a natural way and using a model. To carry out the research, we used the apparatus of probability theory and mathematical statistics. This included using the multidimensional Bayes functional and Bayes hypothesis formula. As a result the author describes the model of biometric etalons of handwritten signatures of the user. The model assumes the use of formulas and calculated coefficients for calculating the distribution of signature features characteristics in the changed state from the values of the signature features in the normal state. The introduction of this model will significantly simplify the procedure for obtaining the etalons in changed state The mentioned above model can find applications in systems of dynamic biometric identification.

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10.1088/1742-6596/1050/1/012076