Development and research of a software system for biometric authentication of a user based on the dynamics of a handwritten signature using fuzzy features

The complexity of reliable biometric user authentication based on the dynamics of handwritten signatures is due to their high intra-class variability associated with changes in the physical and emotional state of a person, as well as writing conditions. Existing approaches do not always provide sufficient accuracy and resistance to these variations. This work is devoted to the study and development of a software system for biometric authentication using the apparatus of fuzzy set theory to improve the reliability of recognition. In this work, we proposed an original feature model of a dynamic handwritten signature, including a set of static and dynamic features, including fuzzy ones, taking into account the uncertainty and variability of handwriting. As a signature standard, we used a set of membership functions built on the basis of the components of the feature model. We proposed an architecture of the recognition system consisting of training subsystems, creating a test signature model, and making a decision on authenticity. We developed a software system that implements the proposed approach using the SciLab mathematical package and the C++ programming language. The system provides the functionality of user registration and formation of a training sample based on signatures entered using a graphic tablet, as well as recognition of test signatures. We conducted an experimental study based on the MCYT Signature 100 signature collection. During the study, we experimentally determined the optimal value of the cluster compactness degree for constructing feature membership functions that minimizes the equal error rate coefficient. The experimental results demonstrate a decrease in the equal error rate coefficient compared to known methods, which indicates the effectiveness of the proposed approach. The use of fuzzy features helps to increase the system's resistance to variations in signatures and, as a result, increase the reliability of biometric authentication in various applications that require identity verification. The results of the study can be used to improve the security of authentication systems and protect confidential information.

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