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Identity verification based on the dynamic signature is an important issue of biometrics. Analysis of the signature dynamics, especially selecting partitions in order to improve processing precision, is very often used to evaluate whether a given signature is genuine. In our previous works we have presented methods for selecting partitions with a specific interpretation. They correspond to low and high velocity of the pen and low and high pressure of the pen in the initial, middle and final time phases of a signing process. In this paper we propose a new partitioning method which selects a specific, i.e. characteristic set of partitions for each user individually. The purpose of this approach is: (a) to increase the precision of signature processing, (b) to individually adapt the method to the specifics of the user signatures by eliminating redundant partitions, and (c) to simplify test signatures verification. The tests of the proposed method were performed using the BioSecure signature database. [ABSTRACT FROM AUTHOR]

Abstract: Verification of identity based on the analysis of dynamic signatures is an important problem of biometrics. The effectiveness of the verification significantly increases when the dynamic characteristics of signature (e.g. velocity of the pen) are considered. These characteristics are individual for each user and difficult to imitate. The effectiveness of the verification can be further improved by using partitioning. In this paper we propose a new method which uses partitioning. In our method partitions represent areas of high and low speed of signature and high and low pen׳s pressure. All selected partitions are used by our algorithm, but more important in the classification process are these partitions, in which the signatures of the user acquired during training phase are more stable. Moreover, final classification is interpretable. In this paper we present the simulation results of the proposed method for the two databases: SVC2004 and BioSecure Database. [Copyright &y& Elsevier]

This paper presents original solutions from the field of intelligent expert systems for use in behavioral biometrics. They combine possibilities offered by biometric methods with the theory of fuzzy sets and the theory of population-based algorithms. Behavioral biometrics is concerned with learned behaviors such as a way of signing, movement, speaking, etc. However, these attributes trend to change over time. This is particularly important in a variety of fields including identity authentication. In this paper we present a new approach to the analysis of changes in behavioral characteristics. The purpose of the proposed approach is to predict values of features describing the so-called dynamic signature. It is the signature represented by waveforms describing a number of features including pen pressure and velocity. The proposed approach can be particularly useful in cases in which the interval between signature acquisition sessions is long. Our approach was tested with the use of the dynamic signature database called ATVS-SLT DB. It seems that the solutions proposed in this paper offer a new and interesting look at the solution of the problem of variability (aging) of biometric features. [ABSTRACT FROM AUTHOR]